Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are designed with the prerequisite that only one speaker is present in the analyzed audio recording. However, some applications involve the need to identify multiple speakers that interact freely in an audio recording. This paper proposes two architectures of speaker identification systems based on a combination of diarization and identification methods, which operate on the basis of segment-level or group-level classification. The open-source PyAnnote framework was used to develop the system. The performance of the speaker identification system was verified through the application of the AMI Corpus open-source audio database, which contains 100 h of annotated and transcribed audio and video data. The research method consisted of four experiments to select the best-performing supervised diarization algorithms on the basis of PyAnnote. The first experiment was designed to investigate how the selection of the distance function between vector embedding affects the reliability of identification of a speaker’s utterance in a segment-level classification architecture. The second experiment examines the architecture of cluster-centroid (group-level) classification, i.e., the selection of the best clustering and classification methods. The third experiment investigates the impact of different segmentation algorithms on the accuracy of identifying speaker utterances, and the fourth examines embedding window sizes. Experimental results demonstrated that the group-level approach offered better identification results were compared to the segment-level approach, and the latter had the advantage of real-time processing. Diarization is an important step in the process of speech recognition, as it partitions an input audio recording into several speech recordings, each of which belongs to a single speaker. Traditionally, diarization combines the segmentation of an audio recording into individual utterances and the clustering of the resulting segments. Segmentation identifies points where speakers change in an audio recording, and the clustering groups speech segments on the basis of the main characteristics of the speaker .Although the development of diarization methods started more than a decade ago, intensive research continues to improve the accuracy and computational efficiency of diarization algorithms. Diarization systems mainly utilize unsupervised machine learning algorithms when utterances are shared between speakers, but it is not known which diarization label applies to a particular speaker. This approach is known as unsupervised diarization. However, in some applications, it is necessary to identify multiple speakers that interact freely in an audio recording. This task can be completed using a supervised diarization approach, which combines diarization and identification methods.The main distinction between unsupervised and supervised diarization involves the different ways of segment indexation. The former relies on grouping of similar segments into separate categories (clustering), while the latter requires matching a segment with a certain speaker on the basis of voice samples (classification). Thus, the last transformation step for supervised diarization is performed with the help of an additional output module—the speaker‘s classifier. However, the identification of the speaker using supervised diarization has a significant peculiarity, as it is necessary, unlike in the common biometric voice recognition systems, to analyze the utterances of more than one subjects. This is a challenge, especially under low-data conditions, as new speakers are represented only by one short audio recording lasting dozens of seconds at most.The application of unsupervised diarization allows for improving the readability of automatic speech-to-text transcription due to the structure of the audio recording upon the queue of speakers . AnotherCompanies and individuals are increasingly interested not only in the audio transcription of conversations coming from online meetings and video conferences, but also in the automatic identification of speakers. These objectives can be achieved using supervised diarization systems, which allow for the identification of speech segments belonging to a particular speaker. Thus, it becomes possible to perform the voice search/indexation of content within recorded conversations .A prevalent supervised diarization use case is the annotation of important phone calls. For such a purpose, a conversation recording of a caller and an operator is partitioned into two speech clips using diarization. Afterwards, those clips are sent to a speech recognition system that provides corresponding speech-to-text transcription. Another exemplary use of supervised diarization is in healthcare and medical services, where the identity of a doctor is known beforehand . This alIn the developing modern digital world, application scenarios of diarization systems are constantly expanding, which often appear as components of more complex information systems in human–machine communication. Therefore, research aimed at the development and implementation of a flexible and open-source system capable of solving the main problems of diarization, both in a supervised and unsupervised manner, is relevant. This article presents the architecture of a diarization system using the PyAnnote framework combined with external components that provide expanded functionality.A review of literature sources gives grounds to state that diarization algorithms are traditionally based on an unsupervised approach ,9,10. StAlthough speaker diarization is usually an audio-related task, a separate field of research, multimodal speaker diarization, has arisen. These systems use some information in addition to the audio to improve diarization quality . These mA number of studies were devoted to the design of systems utilized for the identification of statements in audio recordings belonging to multiple speakers ,19,20. SMost diarization systems work offline, that is, the diarization result is obtained after analyzing the entire audio recording. Online audio diarization is able to output the diarization result of each audio segment immediately after its analysis. Maintaining online diarization accuracy is a challenging task due to the unavailability of data from future audio segments. The creation of online diarization systems opens up new possibilities and extends the range of applications of modern speech processing technologies ,21,22.Diarization systems are used to perform a number of speech processing tasks in various scenarios. For their implementation, proprietary solutions can be used, primarily from such technological IT giants as IBM (IBM Watson) or Google (Google Cloud Speech) . NeverthCommercial products are first designed as solutions for speech recognition, whereas diarization is a secondary feature. Advantages of such solutions are the simplicity of usage, the speed of development, and system adjustment rather than good accuracy. However, there are drawbacks as well: no possibility to adjust the system parameters or its separate components, the price, and a dependency on the infrastructure of the service provider.Taking into account the variety of use cases and trends mentioned in the above design and implementation of a general-purpose system capable of solving both supervised and unsupervised major diarization tasks is a relevant research topic from both theoretical and practical perspectives. It also makes sense to design such a system so that it could process audio data both in online and offline modes. It is reasonable to carry out development on the basis of an open-source framework with a stagewise architecture, as it opens the possibility to reuse existing components and adjust the system’s parameters. Another important point is that the selected framework should demonstrate results comparable to those of commercial products in terms of accuracy. After deeper analysis, the following frameworks were identified as satisfying the above-mentioned demands: PyAnnote, SpeechBrain, and pyAudioAnalysis.PyAudioAnalysis is an open-source library designed for sound processing, namely, for feature extraction from audio recordings with their further classification and segmentation. Thus, such a library may be used for the purposes of supervised diarization. This library has a number of designed tools. First, data analysis and feature extraction from the audio recordings. It also has tools for audio segmentation and classification, but its accuracy is significantly lower than that of the commercial solutions and other open-source solutions .SpeechBrain—is a set of open-source software tools designed for creating an audio analysis system, namely for such tasks as automatic speech recognition, speaker identification, language identification, etc. Currently, this library does not contain any tool for audio segmentation which complicates its application for the diarization tasks .PyAnnote is a directly developed open-source library for diarization tasks ,10,26. IThus, the authors selected PyAnnote for further research, as it provides the highest accuracy  among the available options and is implemented using stagewise modular approach which enables direct hyperparameters’ tuning. Thus, the core requirements formulated above are fulfilled, and PyAnnote can be considered a suitable solution for supervised and unsupervised diarization.The aim of this work was to design a general-purpose diarization system (capable of operating in supervised and unsupervised mode) using the PyAnnote framework. This requires adding extra modules for speaker identification along an adjustment of the system’s parameters on the stagewise level in order to extend its functionality and improve the diarization results.The current research basically uses the unsupervised diarization system (using clustering) on the basis of PyAnnote (version 1.1) presented in Segmentation—module that uses the voice activity detection, speaker change detection, and overlapped speech detection modules to create timestamps that represent segments of speech within an audio recording.(a)Voice activity detection—the module’s function is to detect the time intervals in the audio recording where a human voice is present.(b)Speaker change detection—the module’s function is to detect the moments of time in the audio recording where the speech of one speaker ends and the speech of another one starts.(c)Overlapped speech detection—the module’s function is to detect time intervals in the audio recording where two or more speakers are talking simultaneously. Such segments are removed from analysis, as, in such cases, additional transformations related to the separation of signals overlapping in time are performed to detect a particular speaker.Speaker embedding—a module to create a vector with a certain given dimension, of which the numerical values represent speaker-specific features derived on the basis of physiological voice parameters. Thus, the distance between vectors that correspond to phrases belonging to one person is smaller than the distance to the vectors created from the phrases spoken by the other speakers.Clustering—the module responsible for grouping segments corresponding to the speakers by using their embeddings.The following modules were included in the system composition:Architecture A , SDS A wArchitecture B , SDS B wAt the output of an unsupervised diarization system , the inpIn order to select a particular implementation of the classification algorithm, in a real-world application, supervised diarization systems are constantly tuned in order to support new speakers. Normally, the amount of available data for such tuning is very small (few short phrases at best), which is not enough to train common classification models such as XGBoost, neural networks, and SVM. In our research, 20 s of reference speech was used to add a new user to the diarization system. Due to this constraint, we built custom classifier models using a distance function followed by a threshold. This was applied to both architectures presented in Type A architecture is based on straightforward segmentwise classification. Each segment is matched to a particular speaker on the basis of the distance between the reference embeddings of the speaker and the embeddings of the certain audio segment. Depending on the previously adopted threshold, it is defined whether the segment belongs to the speaker or not. This method is suitable for real-time or online diarization, which means that it can also be used with audio streams and not only audio recordings. Grid search was used for threshold selection. The selected thresholds for the distance functions are the following: Euclidean distance threshold - 7.0; Cosine distance threshold - 0.8; Manhattan distance threshold - 5.0; Pearson’s distance threshold - 0.5; Spearman’s distance threshold - 0.5. Type B architecture is based on the idea of the distance to the group (cluster centroid). In this case, before choosing which segments refer to a particular speaker, all segments are grouped, that is, unsupervised diarization is performed in essence. After audio-segment embeddings are grouped, the centroid for each group is defined ; afterwards, the distance from the target speaker embedding to each of the centroids is calculated, the closest centroid is selected, and the audio segments of its corresponding group are marked as the segments belonging to the target speaker. This method is suitable for offline diarization only  because it performs the analysis of the entire speech recording as a whole.Classifier design using distance functions (Architecture A).Classifier design using clustering algorithm and distance functions (Architecture B).Segmentation optimization to maximize Fscore and minimize DER.Embedding window optimization for the maximization of the F score and the minimization of DER.In order to implement the approach described in the previous section, a set of optimization experiments was performed. We decided to study the impact of separate modules and their parameters on the final results of supervised diarization. Experiments were performed in the following order:The reason for this was to design new components that eere related to supervised diarization, first by using default configuration from unsupervised pipeline for segmentation and embedding modules. Afterwards, an additional optimization for the segmentation and embedding part is run in order to boost the accuracy of supervised pipeline.Voice activity detection: Speaker change detection: Embeddings: DNN x-vectors, Clustering: probabilistic linear discriminant analysis (PLDA).After the implementation and optimization of the supervised pipeline, its results were compared with those of an original unsupervised pipeline. For this purpose, the following default hyperparameters provided by the PyAnnote developers were used:All the experiments were conducted on the same dataset—AMI Corpus, which is an open-source dataset containing audio and video recordings of meetings and gatherings where speech is annotated and each annotation segment is assigned with the identification number of the speaker; thus, these data are suitable for both supervised and unsupervised diarization . Audio dExperiments for the supervised diarization were also conducted for each speaker in each audio recording. For example, an audio recording with 4 speakers was analyzed in turns, and accuracy was evaluated separately for each participant. When the number of speakers is not given, the system must determine it. Ground-truth data are given, and performance metrics are calculated on the basis of unseen data.The following metrics are widely used to evaluate diarization systems : detectiDetection F score—the accuracy score for voice detection in the audio recording.Segmentation F score—the accuracy score for the detection of segment boundaries and overlaps in the audio recording.Diarization error rate—the error of detecting a segment’s boundaries and overlaps in an audio recording considering the true or false assignment of the speaker identifier to the audio recording segment. This is the error main for diarization and is the generally accepted metric in commercial systems.Identification F sscore—the accuracy score for the identification of speakers on each segment in an audio recording, which is calculated considering the duration of each segment.The identification F score (or F1 measure) was selected as the main metric, as it is the one that correctly reflects the accuracy of assigning the speaker identifier to each of the audio recording segments, unlike the diarization error, which uses automatic segment correlation that is not connected to any of the speakers. The diarization error rate was selected as a secondary metric to enable comparison between supervised and unsupervised pipelines.The purpose of this experiment was to study the impact of different distance functions in the context of the supervised diarization accuracy (DER and F-Score). The principle of this distance-based classification is quite simple. We had a built-in reference speaker that played audio received from the reference speaker audio. Further, for each segment in the audio recording, we calculated the distance between the reference embedding and the analyzed segment embedding. If the distance was lower than the defined threshold, the segment belonged to the reference speaker. The following distance functions were studied: Euclidean, Manhattan, Pearson’s, Spearman’s, and cosine ,29 becauThe experiment results show that cosine distance had the best result, just slightly outperforming Pearson’s and Spearman’s distances.Clustering all the audio segments embeddings.Calculating each cluster centroid.Calculating the distance from the reference speaker embedding to each of these centroids, and the closest distance determines which cluster contains segments with the reference speaker.The goal of this experiment was to determine the impact of various clustering techniques on supervised diarization performance. The experiment flow was the following:The following clustering algorithms were chosen: PLDA (default for an unsupervised pipeline), K-means, hierarchical, and spectral because they are the most popular clustering algorithms, and their implementations are available in the scikit-learn library. Taking into account the results of the previous experiments, the cosine function was used for centroid classification. Experimental results are presented in This table shows that K-means clustering dramatically outperformed both the hierarchical and spectral algorithms. A combination of K-means clustering with cosine distance also demonstrated much better results than those of segment-wise classification presented in Experiment 1. Thus, Architecture B was better in terms of diarization accuracy than Architecture A. However, as mentioned earlier, it is not suitable for real-time applications and can be utilized for offline processing only.Standard—the default PyAnnote segmentation that relies on a combination of the voice activity detection (VAD) and speaker change detection (SCD) modules. This implementation is two-stage. First, VAD separates speech and nonspeech segments. Afterwards, speech segments are additionally processed by SCD, which splits them into single-speaker segments.Overlap drop-out—in addition to VAD and SCD, this approach also uses overlap detection  as the last transformation in the segmentation pipeline. If voices are quite similar, there is a greater chance that the speaker change detection would not be able to correctly identify the boundaries. However, after applying overlap detection, such sections can be identified as overlapped speech and can be effectively removed. This results in increased chances of finding speaker change boundaries and thus improving diarization performance.The goal of this experiment was to determine how different segmentation algorithms affect the performance of a supervised diarization pipeline. Taking into consideration the available building blocks, the following modifications of the default PyAnnote segmentation methods are proposed:Optimization was performed separately for the two architectures from The experimental results show that the standard segmentation method just slightly outperformed overlap drop out while being less computationally expensive. So, we continued with the standard PyAnnote segmentation for both SDS architectures.The goal of this experiment was to determine the optimal size of the embedding window. Embeddings were calculated for each individual segment using a rolling window (with no overlap) followed by averaging. For the embedding extraction, the default PyAnnote embedding model was used. It has a tuneable parameter called The performance of supervised diarization was clearly correlated with the embedding window size: the lower the window was, the better the performance. With a chunk size of 1.0 s, the supervised diarization performance increased by almost 4.5% for the supervised architecture of Architecture A, compared to the default chunk size of 2.0 s. The performance increase of supervised Architecture B was negligible compared to the default chunk size, only 0.1%. The larger chunks only worsened the supervised diarization performance.To compare the pipelines, the diarization error rate (DER) metric was chosen, which is widely used in speaker diarization tasks. It is measured as the fraction of time that is not attributed correctly to a speaker or nonspeech. This metric does not, however, require to correctly identify the speakers by definite identification number or name, which renders it suitable to compare unsupervised and supervised speaker diarization systems. For supervised speaker diarization, the F score metric is calculated, as it better reflects the errors of labeling speakers with a wrong ID. There is also a strong negative correlation between F score and DER .The following set of parameters were selected for the SDS of the Type A architecture: cosine distance followed by thresholding as a classification model, embedding window size of 1.0 s, and segmentation based on VAD, SCD, and overlap detection. The length of the reference speaker audio per class is 20 s.For the SDS of the Type B architecture, the following configuration was proposed: cosine distance followed by thresholding as a classification model, K-means as clustering algorithm followed by centroid calculation, embedding window size of 1.0 s, and segmentation based on VAD, SCD, and overlap detection. The length of the reference speaker audio per class is 20 s.The baseline (unsupervised) PyAnnote pipeline from The segmentwise supervised diarization pipeline (type A) yielded a DER of 20.14%, which was an improvement over the baseline by 4.84%, and an F score of 85.89%.The group-based supervised diarization pipeline (Type B) yielded a DER of 15.52%, which was an improvement over the baseline by 9.45%, and an F score of 90.79%.The main sources of performance improvement included the clustering algorithm and distance function. The best clustering algorithm and distance function for the task were the K-means algorithm and cosine distance, respectively. Another major source of improvement was the transition from the unsupervised to supervised method of diarization. Since the model knew the speaker embeddings beforehand, it was easier and more accurate to assign the segments to the correct speaker.Additionally, some steps can be taken to further improve diarization performance: filtering acoustic disturbances, i.e., other nonspeech and environmental sounds, perhaps by using speech separation techniques or other signal processing methods; addressing diction fluctuations or different speaker expressions where one speaker with different expressions could be identified as a different speaker; filtering segment edge effects where speech begins or ends (or briefly overlaps with another speaker), which may affect the speaker embedding, which in turn affects the classification performance; preventig a speech segment from being too short or too long, which also affects the speaker embedding of a given segment.PyAnnote is one of the most common and functional solutions in the field of audio signal processing, particularly involving diarization. Its main advantages over well-known alternatives are implementation in the popular Python language, open-source code under an MIT license, modular architecture with the possibility of adding new modules to the system, and adjusting the parameters of existing components. In addition, PyAnnote is almost as accurate as existing commercial solutions such as IBM Watson or Google Speech.The existing implementations of the PyAnnote library are designed primarily for tasks of unsupervised diarization. At the same time, there is the opportunity to expand the functionality of the system and adapt PyAnnote to tasks of supervised diarization, which offers new potential for speaker identification and searching an audio database for recorded parts belonging to a specific speaker. A new architecture design and the parameter optimization of audio processing pipeline  was the subject of research.The approach proposed in this paper provides the possibility to select between two options for speaker identification using the PyAnnote framework. The first operates in a segmentwise fashion by substituting the clustering module from the original pipeline with a classifier. The second is based on the combination of clustering and classification. In fact, in the first case, speaker identification is performed at the level of individual audio segments, while in the second case, identification occurs on the basis of groups of segments of the same type combined into clusters.In order to investigate the proposed approach for speaker identification and optimize the separate components of the diarization pipeline, four experiments were conducted. All research was conducted on an open dataset, AMI Corpus, which contains the marked audio and video recordings of working meetings and meetings. Metrics such as F score and diarization error rate were used for pipeline evaluation.The first experiment investigated the accuracy of diarization under different implementation classification algorithms for segment-level architecture. In the second experiment, research was carried out to select the optimal clustering algorithm for the group-based architecture of a supervised diarization system. In the third experiment, we examined and selected the optimal segmentation algorithm for the two architectures. The fourth experiment was aimed at choosing the optimal duration of the window of vector embeddings, which is necessary for the optimal vector representation of speakers within audio recording segments.Unsupervised mode: ready to be used out of the box, as no additional activities are required to add a new speaker into the system with DER of about 24.97%.Segmentwise supervised mode: opens up the possibility of real-time operation for speaker identification, and provides a DER of 20.14% and an F value of 85.89%.Group-based supervised mode: provides the highest accuracy yields, a DER of 15.52% and an F value of 90.79%.The proposed approach enabled expanding the functionality of the speaker diarization system based on PyAnnote audio processing by adding three operational modes:In the future, it is appropriate to conduct additional research in order to optimize other modules of the PyAnnote  to further improve diarization accuracy.


This series of articles comprise of short reviews of clinical problems relevant to oral health care in the twenty-first century. The present article uses a composite of presenting case symptoms to hypothetically illustrate differential diagnoses of pain of the tongue and why there may, or may not be, links to aspects of infection of SARS-CoV-2 (COVID-19). Geographic tongue is common, rarely symptomatic and not potentially malignant.Psychological upset can be an aggravating factor for a burning-like sensation in the mouth.Altered taste, in the absence of a COVID-19 infection, is usually the consequence of plaque-induced disease or longstanding oral dryness. A 54-year-old man was referred to an oral medicine service by his general medical practitioner (GMP) as the patient had been experiencing a stinging-like sensation of the dorsum of the tongue for the past four months. He reported previous sore red patches that seemed to arise and diminish spontaneously for many years but the pain that was causing the patient most distress was the continuous stinging sensation across the dorsum of the tongue. The GMP had initially assumed all the symptoms were due to 'thrush' but as nystatin had not lessened the symptoms or signs, he had checked the full blood cell count, haematinics  and random glucose and found these to be normal.The attending clinicians established that the patient had been experiencing intermittent soreness of the dorsum of the tongue over the past 30 years. The symptoms comprised intermittent episodes of mild stinging across different areas of the dorsum that accompanied the emergence of red patches that varied in site and size on the dorsum. The stinging was particularly noticeable when the patient ate tomatoes and/or pizza. However, separate to this, over the past four months, the patient had experienced a mild, burning-like sensation across the anterior dorsum of the tongue and noticed a constant metallic-like taste. This unusual taste was present most of the time, generalised throughout the mouth, had no identifiable local initiating factor, but would lessen for a short time following eating or drinking. The taste disturbance did not correlate with any possible acute COVID-19 illness. The burning-like sensation had started to reduce in the past two weeks but was arising on the anterior half of the dorsum of the tongue. The patient did not report having a dry mouth or any other abnormal sensations to the mouth.The patient was an accountant and married with two children, aged 6 and 12 years. He did not smoke tobacco and drank about seven units of white wine per week. He had no notable features in his medical history. He had not had symptoms suggestive of acute or long COVID disease and had received the second dose of the AstraZeneca (ChAdOx1 nCoV-19) vaccine three months before the worsening of the tongue discomfort and onset of the taste disturbance. His wife and children had not had any symptoms suggestive of acute or long COVID disease. The patient seemed mildly anxious and worried. Clinical examination revealed no cervical lymphadenopathy or enlargement of the major salivary glands. Intraorally, the mouth was not dry and there were no features of long-standing oral dryness, nor evidence of plaque-induced gingival or periodontal disease. All permanent teeth were present other than the third molars (these had been extracted many years ago). The dorsum of the tongue was mildly fissured, particularly at the lateral aspects and there were some areas of circular red areas with a slight yellow-to-white border. The tongue was soft and mobile and there was no evidence of oral mucosal or gingival speckling. There was no clinically obvious local cause for the dysgeusia or burning sensation of the tongue.The working diagnosis of the red patches and fissuring of the tongue was erythema migrans (geographic tongue), while the working diagnosis of the burning sensation of the tongue and dysgeusia was burning mouth syndrome (BMS). There was no requirement for additional investigations as the only relevant haematological/serological investigations had been found to be normal by the patient's GMP and there were no logical differential diagnoses that required to be considered. Treatment comprised providing the patient with clear information concerning both diagnoses (detailed later). There was no need for any additional intervention with regard to the BMS, while with regard the erythema migrans, the patient was advised to avoid, if possible, foods that caused discomfort to the tongue and to dab benzydamine hydrochloride onto any areas that caused distress.The patient was reviewed three months later, at which time, he reported that the symptoms and signs of the erythema migrans were continuing but he was now endeavouring to avoid eating foods that worsened the discomfort. The burning-like sensation and metallic taste had gradually dissipated.1 but one key aspect that has received scant attention is how little acute COVID disease directly affects the mouth - other than giving rise to altered taste and smell - and how little is known about how long COVID disease will impact upon the mouth and oral health care.The COVID-19 pandemic has had a significant adverse impact upon all aspects of life across the globe. With regard to oral healthcare, the greatest impact has been the cessation of aerosol generating procedures, the need for social distancing within the clinical setting and the need for fallow times when operative clinical services recommenced. There are many other downstream effects upon oral healthcare that include: increased disease burden ; limited or no immediate patient access to oral health care; loss of income and employment opportunities of staff; and a fall in morale of many oral healthcare providers. Each of these are well-detailed elsewhere2 The precise duration varied between individuals and could, or can be, long-standing and hence considered part of long COVID disease. The precise cause of this altered sensation is unknown but probably reflects direct viral damage to, for example, the olfactory bulb or neurons of the cleft,3,4 possibly having genetically determined risk factors.5 As yet, there remains no known therapy for loss of taste and/or smell - now termed post-infectious olfactory function - although a number of strategies to treat this have been suggested.6 Anecdotally, there is evidence that infection with the omicron variant of COVID-19 does not give rise to the altered taste and/or smell as frequently as that of the delta variant.Certainly, in the early phases of the COVID-19 pandemic, it was suggested that up to 60% of infected individuals had altered smell and/or taste sense. These varied between patients and could comprise complete loss of smell (anosmia) and/or taste (ageusia), partial loss  or distortion of taste (parosmia). No one taste sense is particularly affected.7 A number of such possible aggravating non-physical factors have emerged as a consequence of the potential effect of the COVID-19 pandemic upon individuals, their families, and society, hence it is probable that more patients than ever will develop symptoms akin to BMS.The present patient reported the onset of dysgeusia and a burning sensation. As the sensation was of a burning quality that extended across the entire dorsum, had no identifiable precipitants or relieving factors, was unrelated to the geographic tongue and accompanied by dysgeusia, it was appropriate to consider the overall diagnosis as BMS. This disorder is not uncommon, tends to arise in middle-to-late life, may be more common in women than men and can be associated with a wide range of potential aggravating factors.8Prior to the onset of the COVID-19 pandemic, the symptom of a persistent altered taste was considered uncommon. Most taste changes are short term, reflect local causes  and do not give rise to an altered taste throughout the mouth. A persistent and generalised altered taste may be a feature of long-standing oral dryness (of many causes). A metallic taste can arise with some drugs, for example, metronidazole, but the present patient did not have plaque-induced oral disease, was not receiving medication and did not have features of a long-standing dry mouth.7While there are many other, usually rare, causes of altered taste, it was concluded that this symptom reflected some likely distress - as reflected by the accompanying burning/stinging-like sensation of the tongue. This was based upon: 1) the patient was outwardly worried; 2) there was no local or common systemic explanation for the symptoms; 3) there was no correlation with any acute or long COVID disease; and 4) the taste sensation was metallic in quality. In BMS, the dominant symptom is one of burning that may be transiently relieved by eating and/or drinking but not improved with simple analgesics. This is often accompanied by other features; indeed, these are often the more frequent and/or dominant and may include a burning-like sensation of the tongue and other intraoral sites, a sensation of oral dryness in the absence of signs of a lack of saliva  and/or sensation of a lump or restriction in the pharynx.7 In the present instance, the burning sensation and metallic taste abated gradually over a three-month period. The possible stressors were not explored but in the age of the COVID-19 pandemic, there are many to consider, each possibly unique to an individual. Note that at no point has the term 'reassurance' been used. This is perhaps an overly used term in healthcare. Reassurance assumes that a patient is indeed reassured by information - a more appropriate phrase might be 'information was provided that met the patient's present self-reported needs'.There remains no one specific therapy for BMS and related symptoms and while the literature suggests that this is often a problem that may persist for many years, it is often aggravated by a variety of factors, - known or unknown to the patient. While aggravating factors  cannot be miraculously resolved, it is always possible to inform the patient of the diagnosis and indicate that there is no overt disease of the mouth (or indeed elsewhere) causing this symptom. Indeed, sometimes this is enough to dampen down the symptom or indeed resolve it. Where this is not possible, there are a variety of strategies that can be applied in the specialist setting to aid the patient.Erythema migrans (geographic tongue) is common, affects all genders and ethnicities and probably arises in childhood or early adulthood. The signs  may occasionally be brought on by certain foods  and the symptom of stinging may be worsened by hot, citrus or spicy agents. It has few associations with systemic disease. Many individuals with erythema migrans have no associated symptoms and only develop discomfort when they change their diet  but for those with symptoms, there is no specific therapy other than avoidance of possible precipitants and application of a local analgesic agent (for which there is no evidence-base). Certainly, this disorder is not due to fungal infection, is not potentially malignant and not related to acute or long COVID disease.9,10,11 Oral dryness, described as 'xerostomia', is likewise not unexpected, as is gingival swelling, probably due to plaque. At present, the only consistent and well-described oral feature of such infection is post-COVID-19 rhino-orbito-cerebral mucormycosis that has affected individuals in parts of India. This systemic mycotic may cause trigeminal neuropathy and 25% of affected persons have had destruction of palatal bone. It has arisen on a background of diabetes mellitus, high-dose corticosteroids, long-term hospitalisation and vaccination.12,13,14,15 COVID-19 vaccines very rarely give rise to any adverse effects upon the mouth.16,17Of interest, perhaps, is that some national newspapers suggested that erythema migrans was a feature of acute COVID disease - it is not. Acute COVID disease probably makes little direct impact upon the mouth. The small number of reports of possible manifestations of acute COVID are not notably detailed, although areas of non-specific erythema and ulceration of the oral mucosa have been described. Extensive ulceration, probably due to erythema multiforme, has been reported, which is perhaps unsurprising given the wide array of drugs that patients may receive for the management of SARS-CoV-2 infection.This case demonstrates a number of key points relevant to oral health care: 1) erythema migrans is common but can be confused sometimes with other disorders that cause pain of the tongue; 2) burning mouth syndrome can be accompanied by a gamut of oral symptoms other than just pain; 3) distress, caused or aggravated by many factors, can give rise to BMS and related disorders; and finally 4) while acute and long COVID disease have little direct effect upon oral function, other than causing dysgeusia, the very many concerns of living in the age of COVID-19 may sometimes give rise to unusual oral symptoms.


Recall and detection Of breast Cancer in Screening (ROCS) trial uses a novel data acquisition system that minimizes these limitations while obtaining an estimate of the RR-CDR curve during actual practice in the Dutch National Breast Cancer Screening Program. ROCS involves collection of at least 40,000 probability-of-malignancy ratings from at least 20 radiologists during interpretation of approximately 2,000 digital mammography screening cases each. With the use of custom-built software on a tablet, and a webcam, this data was obtained in the usual reading environment with minimal workflow disruption and without electronic access to the review workstation software. Comparison of the results to short- and medium-term follow-up allows for estimation of the RR-CDR and receiver operating characteristics curves, respectively. The anticipated result of the study is that performance-based evidence from practice will be available to determine the optimal operating point for breast-cancer screening. In addition, this data will be useful as a benchmark when evaluating the impact of potential new screening technologies, such as digital breast tomosynthesis or artificial intelligence.The magnitude of the tradeoff between recall rate (RR) and cancer detection rate (CDR) in breast-cancer screening is not clear, and it is expected to depend on target population and screening program characteristics. Multi-reader multi-case research studies, which may be used to estimate this tradeoff, rely on enriched datasets with artificially high prevalence rates, which may bias the results. Furthermore, readers participating in research studies are subject to “laboratory” effects, which can alter their performance relative to actual practice. The • The ROCS trial aims to estimate the recall rate–cancer detection rate curve during actual screening practice in the Dutch National Breast Cancer Screening Program.• The study design is aimed at avoiding the influence of the “laboratory effect” in usual observer performance studies.• The use of a tablet and a webcam allows for the acquisition of probability of malignancy ratings without access to the review workstation software.The online version contains supplementary material available at 10.1007/s00330-022-08820-5. Combined with improvements in treatment, mammography-based screening has been credited with an important decrease in breast cancer mortality over the last two decades . NonetheIn the Netherlands, a publication by Otten et al  has beenRecall and detection Of breast Cancer in Screening (ROCS) trial is to characterize the underlying relationship between these measures in screening practice.Over the years since, the recall rate in the Netherlands has varied, typically with somewhat higher observed values than the recommendation. In 2016, the program recall rate was 2.43%, with a corresponding detection rate to 7.0 cancers per 1000 women screened [laboratory effects that may lead to biased results. Typically, laboratory studies involve the enrichment of the testing set with additional examples of positive cases, which can also lead to prevalence biases in reader responses [Audit data from screening practice is commonly used for determining program performance. However, audit data produces a single estimate of the recall/detection pair, and therefore has limited value for evaluating the tradeoff between recall and detection rates. To evaluate this tradeoff, a probability of malignancy (PoM) score is needed that can be compared to different thresholds for recall, but these are not obtained as part of screening practice. In essence, this PoM reflects the reader’s internal decision process, before applying a threshold to dichotomize their decision into a recall/no-recall outcome –6. Laboresponses .The ROCS trial avoids the potential issue of laboratory effects and the difficulty of extrapolating current performance into predictions of performance at different operating points by using a novel system to acquire PoM scores during actual screening practice. The system is designed to minimally interrupt clinical workflow, and to automatically associate the PoM response with the case identifier. In this Special Report, we present the design of the ROCS trial, aimed at determining the performance tradeoffs for digital mammography interpretation in the actual breast cancer screening environment of the Dutch National Breast Cancer Screening Program. At present, the first stage of data acquisition, the accrual of reader case scores during screening is completed, and the results of this stage are presented. The second and final stages of data acquisition, involving the determination of the ground truth for the recalled cases and, then, after the 2-year waiting period, for the non-recalled cases, are ongoing.No external funding was needed or obtained for the performance of this study. The study was waived of the requirement of ethics approval and informed consent by the research ethics committee of the Radboud University Medical Center.The primary aim of the ROCS trial is to estimate the trade-off between recall rate (RR) and cancer detection rate (CDR) by estimating the RR-CDR curve for the average radiologist in the Dutch National Breast Cancer Screening Program directly from screening interpretations. Secondary endpoints include estimating the ROC curve for the average breast radiologist during actual screening interpretation and determining if reader performance changes as they progress within one reading session, as previously reported .n = 20) for each and every case  they interpret during several screening reading sessions (approximately n = 8), with minimal interruption or diversion from their usual reading workflow. The outcomes of the cases included in the ROCS trial will be obtained from short-term (1 year) follow-up of the recalled cases via the screening organization and long-term (2 years) follow-up via the Netherlands Cancer Registry.For this, ROCS involves obtaining a PoM rating from screening radiologists  and medio-lateral oblique (MLO) views, are performed at dedicated screening centers equipped with the same imaging systems . The systems are all under the same quality control protocol, performed by the same group of physicists at the Dutch Expert Centre for Screening (LRCB) for the nationwide program. The technologists that acquire all images are specifically trained and undergo regular refresher education and auditing on screening mammography. Cases are interpreted in batches, at dedicated reading centers, usually within 1 or 2 days of acquisition. All reading is performed by certified screening radiologists, who undergo additional periodic training and evaluation to be a part of the screening program. Throughout the program, the mammograms are viewed on a SecurView mammography screening workstation (Hologic Inc.) with dual-head 5-megapixel monitors . The mammography monitors used are also tested by the physicists of the LRCB, as per established protocols.Although the Dutch National Breast Cancer Screening Program involves independent double reading of all cases with consensus or arbitration for disparate decisions, ROCS is focused on individual reader performance. This is because the overall aim of this endeavor is to gather the evidence needed to inform the setting of new performance guidelines for radiologists. Therefore, all performance estimates are performed at the level of individual readers, and then these are aggregated into results for the screening program as a whole. In addition, due to the nature of screening, all acquisition and analysis are performed at the per case level, as opposed to per breast or per lesion.For ROCS, a total of 40,000 cases were needed and were aimed to be included, as determined by the sample size calculation, described below. All cases included for interpretation were eligible for inclusion, with no exclusion criteria. There was no attempt to order the cases or to balance the case sets for different properties of the screened women.n > 3,000 screens per year) to participate in the program. To maximize the diversity in geographic, socio-economic, and other factors in the screening cases, radiologists from five different reading centers around the country were targeted to participate in ROCS. In total 21 radiologists were recruited to participate in the study, since we aimed to recruit at least 20 radiologists.All radiologists participating in the Dutch National Breast Cancer Screening Program were considered eligible, since they already have to meet minimum training, continuing certification, and case volume requirements  to +100 , with zero in the center (uncertain), resulting in 201 possible PoM scores. This large rating scale was patterned after prior work [Therefore, a simplified graphical user interface (GUI) on a tablet  was developed and implemented for this study Fig. . The intior work . The radInvitation Number is acquired. To minimize the added process, no confirmation of the PoM selection nor tapping on a Save nor Next button is necessary. Once a rating is entered, the GUI counts down three seconds, and unless the user stops the countdown, the data is saved, and the GUI is ready for the next case. If the radiologist wanted to change the rating entered, they were instructed to re-enter a rating, and only the last one given for the same case would be considered. Together with the photo and the PoM rating, the program automatically records the time the rating is entered, allowing for analysis of timing.To ensure that the recorded ratings are paired with the correct screening case, the tablet is connected to a webcam , positioned so that a photograph of the area of the screen that displays the A pilot test was performed by having one participating screening radiologist use the tablet during one of his regularly scheduled screening reading sessions. The session involved the reading of 164 cases, and aside from the use of the tablet as described, all other characteristics of the session were normal. Comparison of the total time for this session to previous ones resulted in an estimated slow-down by the use of the tablet of 8%.The primary endpoint of ROCS is the estimation of the curve describing the relationship between recall rate and cancer detection rate for the average reader in the Dutch National Breast Cancer Screening Program. From this curve, it would be possible to determine the benefit, or lack thereof, in increased number of cancers detected at screening due to an increase in the recall rate. In addition to the PoM ratings from the exam interpretations, constructing this curve involves obtaining the outcome of the cases recommended for recall by each participating radiologist. This data is available through the screening organization. As per the programmatic definition, breast cancer diagnosed within 12 months of the woman being recalled from screening is defined as a screening-detected cancer.not visible, present with minimal signs, or as missed. This is a common procedure in the Dutch National Breast Cancer Screening Program, performed for education and quality assurance purposes. Additional ROC curves will be constructed, one excluding interval cancers deemed not visible and one involving only invasive cancers.As related secondary endpoints of the ROCS trial, two different average-reader ROC curves will be estimated to better characterize performance of readers in the screening program. The first will be constructed considering all cancers as defined by the program, i.e., all breast cancers diagnosed within 24 months after the date of screening acquisition. Interval cancer cases will be identified through linkage with the Netherlands Cancer Registry after the necessary waiting period. However, it is known that approximately half of all interval cancers are visible at the prior screening . To idenA model of breast-cancer screening was derived and used to evaluate the number of radiologists and screening exams that would generate a RR-CDR difference curve of sufficient precision. The model is only briefly described here, but see the accompanying Using this model, Fig. The PoM ratings for each radiologist will be used to construct recall and detection rate curves similar to those shown in Fig. To date, the data acquisition at the reading centers has been completed. In total, 21 screening radiologists from five screening centers participated in the study between 19 March 2019 and 6 June 2019, resulting in the acquisition of a total of 42,215 recorded radiologist PoM scores and timestamps. The average number of records per radiologist was 2,010 entries , with 14 radiologists recording more than 2,000 cases. The completion date for each radiologist varied, depending on their reading volume during the study period. In addition, the final total number of cases scored by each radiologist varied, since whole sessions were recorded, instead of data accrual stopping mid-session if 2,000 cases were reached.The distribution of the recorded scores is shown in Fig. The final results of the trial will be available once the short- and long-term waiting periods for collection of the actual outcomes are known.The ROCS data acquisition approach acquires PoM scores and timestamps from radiologists in actual practice as they read screening mammograms. The use of external hardware, in this case a tablet and a webcam, allows for recording this data without having to modify protected and often proprietary workstation software to record a rating and timestamp per case. A simple GUI that only involves swiping on a touchscreen to enter the response, with automatic assignment of a timestamp, results in minimal intrusion of the workflow. A photographic confirmation of the case identifier avoids the need for the reader to confirm the entry or to manually move to the next case, further minimizing the disruption of the workflow. The participating radiologists reported that the procedure was not intrusive to their work.The ROCS data collection procedure is well suited for high-volume image interpretation studies. In national screening programs, as in the Netherlands, this makes feasible the collection of such data from multiple sites for comparison. The flexibility of locating and aiming the webcam at any area of a computer monitor should allow for a similar setup even in more heterogeneous reading environments. Given that the rest of the data collection procedure is independent of the review workstation setup, the approach used in the Netherlands should be feasible for similar studies based on case ratings acquired during practice. As a result of this study, it will be possible to evaluate the tradeoff between recall rate and cancer detection rate, based on actual screening data, avoiding, or at least minimizing, any “laboratory” effect .One alternative to this approach would be to construct a curve from a plot of RR/CDR pairs collected from an international group of screening programs. Since different programs operate with differing recall rates, this would give some indication of the tradeoff between recall and detection rates. However, it has been shown that there is no association between recall and detection rate internationally , althougOnce such an RR-CDR and/or ROC curve is constructed, then the optimal operating point for the program can be selected based on outcomes in a data-driven manner. Knowledge of the optimal operating point could then be used to develop guidance for radiologists regarding their recall rates. This process assumes that radiologists are willing to implement these recommendations. But the change in recall rates after the recommendations derived from the Otten et al study supports the idea that Dutch radiologists are capable of adjusting their performance in response to screening program priorities. In addition, the Netherlands is evaluating the possibility of a transition to digital breast tomosynthesis and/or incorporation of artificial intelligence–based approaches for breast-cancer screening, as in other countries throughout the world. Therefore, in the future there will likely be a need to re-evaluate the trade-off between cancer detection and recall rate for new screening methodologies. If so, it would be ideal to undertake that evaluation having a clear understanding of this same trade-off in the current implementation of breast-cancer screening.ESM 1(DOCX 190 kb)


There are still not enough studies on the prediction of non-utilization of a complication test or a glycated hemoglobin test for preventing diabetes complications by using large-scale community-based big data. This study identified the ratio of not taking a diabetes complication test (fundus examination and microprotein urination test) among adult diabetic patients over 19 years using a national survey conducted in South Korea and developed a model for predicting the probability of not taking a diabetes complication test based on it.This study analyzed 25,811 subjects who responded that they had been diagnosed with diabetes by a doctor in the 2020 Community Health Survey. Outcome variables were defined as the utilization of the microprotein urination test and the fundus examination during the past year. This study developed a model for predicting the utilization of a diabetes complication test using logistic regression analysis and nomogram to understand the relationship of predictive factors on the utilization of a diabetes complication test.The results of this study confirmed that age, education level, the recognition of own blood glucose level, current diabetes treatment, diabetes management education, not conducting the glycated hemoglobin test in the past year, smoking, single-person household, subjectively good health, and living in the rural area were independently related to the non-utilization of diabetes complication test after the COVID-19 pandemic.Additional longitudinal studies are required to confirm the causality of the non-utilization of diabetes complication screening tests. The International Diabetes Federation 2019) (9 (1) estIt is believed that complications related to diabetes  will increase rapidly along with the increase in the prevalence of diabetes. Numerous studies –5 have sHowever, these recommendations are not well followed by diabetic patients living in local communities. It has been reported that 60% of diabetic patients in South Korea do not meet the blood glucose regulation goal  and onlyMany previous studies –13 indicParticularly, the importance of managing diabetic patients’ health at home is increasing due to the extended COVID-19 pandemic that started in November 2019 and preventive measures against it such as social distancing. This study identified the ratio of not taking a diabetes complication test among adult diabetic patients over 19 years using a national survey conducted in South Korea and developed a model for predicting the probability of not taking a diabetes complication test based on it.This study is an epidemiological study using secondary data based on the 2020 Community Health Survey. The Community Health Survey is a survey conducted under the supervision of the Korea Disease Control and Prevention Agency to produce health statistics necessary for establishing local health and medical plans and performing health services. The 2020 Community Health Survey extracted sample locations from the sampling frame, which was generated by linking the housing data and resident registration demographic data. They were complete enumeration surveys and they targeted adults (19 years or older) based on the resident registration in all municipalities  in South Korea. These sampling locations were allocated to each region . Sample households were selected using a systematic sampling method by identifying the number of households chosen as sample points. This study collected data using the computer assisted personal interviewing method, in which a trained investigator conducted a 1:1 interview with a subject using a laptop computer. Finally, this study analyzed 25,811 subjects among 26,839 people who responded that they had been diagnosed with diabetes by a doctor in the 2020 Community Health Survey after excluding 1,028 who had answered items related to the utilization of the microprotein urination test and fundus examination insufficiently.Outcome variables were defined as the utilization of the microprotein urination test and the fundus examination during the past year (yes or no). The utilization of diabetic eye complication examination was defined by the positive answer (“yes”) to the question (“Have you ever received a fundus examination to find whether you have an eye complication due to diabetes in the past year?”). The utilization of diabetic kidney complication examination was defined by the positive answer (“yes”) to the question (“Have you ever received a microprotein urination test other than the urine test stick to find whether you have a kidney complication due to diabetes in the past year?”).By referring to Andersen’s health service utilization model , explanaThis study developed a model for predicting the utilization of a diabetes complication test (kidney disease and eye disease) using logistic regression analysis to understand the relationship of predictive factors on the utilization of a diabetes complication test. The backward selection method was used to select variables in the regression model. This study presented an adjusted odds ratio (AOR) and 95% confidence interval (CI) by developing an adjusted multivariate model that corrected all confounding factors.The developed model (multivariate model) for predicting diabetes complications test was converted into a nomogram so that medical workers could easily interpret the predictive probabilities of high-risk groups. The nomogram developed in this study consisted of four elements. First, the point line is a line placed at the top of the nomogram to derive a score corresponding to a category of risk factors. It is between 0 and 100 points in the logistic nomogram. Second, the number of risk factor lines was as many as the number of risk factors. Third, the total point line is the sum of all individual risk factors and is at the bottom of the nomogram. Fourth, the probability line was placed at the bottom of the nomogram to derive the non-utilization probability of the diabetes complication test. This study presented only the top ten variables with high weights for the nomogram to make interpretation easier.This study used 10-fold cross-validation to test the accuracy of the developed predicting the utilization of a diabetes complication test nomogram to minimize the risk of overfitting. This study presented the area under the curve (AUC), general accuracy, precision, recall, and calibration plot. AUC refers to the area under the receiver operating characteristic (ROC) curve. It is the most commonly used evaluation method in binary classification. It is defined as diagnostic accuracy. When the value is closer to 1, the diagnostic performance gets higher. A calibration plot is to visually confirm the degree of agreement between the prediction probability and the actually observed probability in the nomogram.Also, the utilization of diabetic kidney disease complication test was significantly affected by the recognition of own blood pressure level, the recognition of own blood glucose level, non-drug treatment for diabetes, oral diabetes medication, diabetes insulin treatment, diabetes management education, the completion of diabetes management education, the number of glycated hemoglobin tests in the past year, age, household composition, economic activity, education level, marital status, subjective health status, regular moderate level exercise, smoking, gender, and residential area (p<0.05).The results of logistic regression analysis for predicting the non-utilization of complication tests (kidney disease and eye disease) in South Korean diabetic patients are presented in The predictive performance of the developed two nomograms for predicting the non-utilization of diabetic complication tests was examined by using calibration plots  by using a national survey data representing South Korean communities and provide evidence for effectively preventing complications in diabetic patients. This study had several limitations. First, this study had a probability of underestimation or overestimation due to the recall bias of the survey. It is because this study used face-to-face questionnaires for the utilization of the fundus examination and microprotein urination test. Therefore, future studies need to reduce the probability of recall bias by including a medical record in addition to questionnaires to accurately determine the utilization of the complication screening test. Second, the predictive model does not include information about the duration of diabetes because the Community Health Survey, the data source of this study, did not investigate it. Future studies need to evaluate the duration of diabetes to improve the predictive performance of a model for predicting the non-utilization of complication screening tests in diabetic patients. Third, in this study, we did not reflect the impact of COVID-19 on the testing of complications in patients with diabetes in their analysis. Fourth, since it was a cross-sectional study, even if factors related to the non-utilization of diabetes complication tests were identified in this study, it cannot be interpreted as a causal relationship according to temporal precedence.This study confirmed that age, education level, the recognition of own blood glucose level, current diabetes treatment, diabetes management education, not conducting the glycated hemoglobin test in the past year, smoking, single-person household, subjectively good health, and living in the rural area were independently related to the non-utilization of diabetes complication test after the COVID-19 pandemic. Therefore, it is needed to detect groups that are highly likely not to utilize diabetes complication screening tests by considering these multiple risk factors. It is also necessary to devise a customized support system at the primary care level that can increase the rate of utilizing them while considering these factors. It is required to prepare a system that can systematically monitor groups highly vulnerable to diabetic complications in medically underprivileged areas such as rural areas. Furthermore, since diabetes complication screening tests and blood sugar management are important elements in diabetes management, it is necessary to develop customized diabetes management guidelines that consider the characteristics of those who have utilized diabetes complication screening tests.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Korea Disease Control and Prevention. The patients/participants provided their written informed consent to participate in this study.The author confirms being the sole contributor of this work and has approved it for publication.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education  and 2022 Development of Open-Lab based on 4P in the Southeast Zone.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Covid-19 causes neuro/psychiatric problems by cerebral hypoxia.My therapy could allow us to cross Covid-19 infection by minimizing both immediate and chronic lung damage and would avoid many deaths and neuro/psychiatric problems from cerebral hypoxia.In the case of a Covid-19 lung infection, the virus infects type II alveolar cells which consequently reduce the production of pulmonary surfactant. The surfactant has the function of reducing the surface tension of the alveoli. The less pulmonary surfactant there is, the more the alveoli tend to collapse due to the increased surface tension of their surface. Consequently, the lung would tend to collapse, that is, to reduce its volume, but collapse is prevented by the muscular movement of inspiration, which instead increases its volume. This means that a “low-pressure area” is created in the interstitial space which attracts liquid and substances which are often inflammatory and which organize over time, giving rise to interstitial pneumonia.I propose to administer the pulmonary surfactant to the patient Covid-19 in the presence of dyspnea and certainly during assisted ventilation. This technique is routinely used in preterm infants suffering from lack of pulmonary surfactant production due to the immaturity of type II alveolar cells, pending that once matured these cells produce it autonomously.Similarly, the administration of surfactant during Covid-19 lung infection would allow the correct amount of surfactant to be maintained during the acute phase of the infection and would give time for type II alveolar cells to heal and independently resume surfactant production.No significant relationships.


This paper proposes a network model framework based on long and short-term memory (LSTM) and conditional random field (CRF) to promote Li-ion battery capacity prediction results. The model uses LSTM to extract temporal features from the data and CRF to build a transfer matrix to enhance temporal feature learning for long serialization prediction of lithium battery feature sequence data. The NASA PCOE lithium battery dataset is selected for the experiments, and control tests on LSTM temporal feature extraction modules, including recurrent neural network (RNN), gated recurrent unit (GRU), bi-directional gated recurrent unit (BiGRU) and bi-directional long and short term memory (BiLSTM) networks, are designed to test the adaptability of the CRF method to different temporal feature extraction modules. Compared with previous Li-ion battery capacity prediction methods, the network model framework proposed in this paper achieves better prediction results in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) metrics. However, as a product of industrialization, the performance of lithium batteries4 decreases with increasing usage time. As the capacity of the battery decreases, the battery will eventually fail. Therefore, it is important to predict the future health5 of lithium batteries to improve the reliability of energy systems.Rechargeable lithium-ion batteries play a crucial role in many modern applications6 is essentially a regression problem. Current methods for capacity decay estimation mainly include equivalent circuit models, electrochemical models and data-driven models7.The future health of Li-ion batteries is usually measured by the indicator state of health (SOH), which reflects the battery's usage by calculating the decay process of the battery capacity and obtaining the difference between the battery's current usage state and its factory characteristics. When the current capacity decreases from 80 to 70% of the nominal capacity, the performance of the battery will decline rapidly. When the capacity of a lithium battery decreases to 70%, it is usually regarded as a battery failure. Therefore the accuracy of battery capacity prediction often affects the SOH calculation results, leading to bias in the estimation of battery usage and affecting the subsequent predictive maintenance strategy and energy system management. Li-ion battery capacity prediction9. Wei et al.10 used the Thevenin model by constructing the battery health index online, using the health index as the input parameter and the battery capacity decay as the output parameter in a gray neural network model to estimate the battery SOH. He et al.11 compared resistor-circuit equivalent circuit models of different orders, and adding RC series modules using the high nonlinearity of the battery operating characteristics can improve the accuracy and reliability of the model prediction, but reduces the applicability of real-time12. The capacity prediction method of the equivalent circuit model has the advantages of fewer parameters and high timeliness, but it is difficult to achieve higher prediction accuracy because it ignores the effects caused by environmental changes and data acquisition during the operation of lithium batteries14.The equivalent circuit model uses circuit elements with empirical nonlinear parameters to build a mathematical model representing the battery system and combines measured data to estimate battery capacity and SOH15. Zhang et al.16 improved the prediction accuracy by analyzing the impedance characteristics based on the changes in the impedance properties of the battery. Ashwin et al.17 established an electrochemical battery aging model under cyclic loading conditions and constructed the capacity decay of the lithium-ion battery process. The electrochemical modeling method can accurately describe the movement pattern of positive and negative electrodes and the changing trend of active substances during the capacity decay of lithium ions by analyzing the detailed internal electrochemical reaction process and reaction intensity during the aging process of the battery. However, the electrochemical system is more complex and the characteristic parameters are coupled with each other, which makes its dynamic prediction accuracy poor and difficult to achieve wide applicability.The electrochemical model establishes a physical model for SOH estimation and prediction by studying the chemical processes occurring inside the battery during operation19. Depending on the data mining methods, they are mainly divided into statistical filtering methods, support vector methods, neural network methods, and fusion methods. Statistical filtering is a method to extract and reproduce valid signals and waveforms from data containing a large number of noisy signals, and the best weighting factor with a strong target following ability is automatically calculated and determined by a recursive linear data processing algorithm21. He et al.22 used the extended Kalman filtering (EKF) algorithm to estimate the unknown parameters in the time degradation parameter model of lithium-ion battery capacity and obtained the future phase prediction results of the degradation trend of the remaining battery capacity. Support vector machine (SVM) as a nonlinear data analysis method, can not only provide relatively accurate estimation and prediction results with a small amount of data but also improve the data quality to a certain extent and overcome the drawback that the model falls into the optimal local extremes. Fewer unknown parameters and high sparsity are the characteristics of this method24. Zhang et al.25 used to improve the prediction performance and operational efficiency of the battery by optimizing the relevance vector machine (RVM), by optimizing the RVM. Gao et al.26 proposed a single radial basis kernel function based on the novel multicore SVM based on polynomial kernel and radial basis kernel function for predicting the remaining useful life (RUL) of Li-ion batteries, which has better prediction accuracy and stronger generalization ability compared to SVM while reducing training time and computational complexity. A neural network is a nonlinear prediction method composed of many neurons according to certain rules. The neurons contained in the network model are trained to connect weights and thresholds to build an accurate estimation and prediction model28. Increasing the depth of the neural network can approach any nonlinear mapping with a simple structure and high learning ability30. Neural network approaches mainly include artificial neural networks (ANN), convolutional neural networks (CNN), back propagation neural networks (BP), gated recurrent units (GRU), and long short-term memory networks (LSTM). Zhang et al.31 used LSTM and RNN networks to capture the long-standing relationship between lithium battery capacity degradation for prediction. Fan et al.32 proposed a GRU-CNN network for learning shared information and time dependence of charging profiles, including characteristic variation profiles such as voltage, current, and temperature, for estimating SOH. Zhou et al.33 improved the prediction accuracy of the model by capturing the local capacity regeneration phenomenon generated by the battery during charging and discharging through time convolutional networks (TCN). The fusion method is based on the characteristics of different algorithms, each taking their strengths for fusion improvement, which not only ensures the accuracy of the predicted data but also provides an accurate assessment of the prediction uncertainty. Liu et al.34 proposed a fusion algorithm based on least squares support vector regression (LSSVR) and hidden markov model (HMM) to predict the health status of rolling bearings, where LSSVR was used to predict the feature signal, and HMM was used to identify state features. Hong et al.35 proposed a fusion estimation method for SOH of lithium-ion batteries based on capacity incremental analysis and a weighted Kalman filter algorithm, which has higher prediction accuracy compared to the common Kalman filter method. The recent Li-ion battery capacity prediction models are detailed in Table The data-driven model establishes the mapping relationship between the characteristic parameters and the health condition from the overall level of the data by extracting the characteristic values of the measured parameters47. CRF constructs the state transfer matrix by the trend of the changing relationship of the neighboring labels and obtains the probability distribution of the prediction sequence by reverse decoding, where the state sequence with the highest probability is the optimal prediction result. The model adds CNN networks to learn feature data at different scales and LSTM networks to collect time-series relationship information. The feasibility and effectiveness of the model were verified on the lithium battery dataset provided by NASA, and the prediction accuracy of the model was improved compared with other network models. The CNN-LSTM-CRF model provides a new idea for the lithium battery prediction problem.To improve the accuracy of lithium battery capacity prediction, this paper proposes a Li-battery capacity prediction model with CRF as the core. CRF is a discriminative probabilistic model about the temporal sequence, which is widely used in natural language processing (NLP)The CRF method is attempted to be introduced in the capacity prediction problem to calculate the observed state of the capacity prediction sequence by the offset matrix of the feature data, which more intuitively reflects the change of the capacity decline trend.To improve the prediction accuracy of the CRF model, the study incorporates a CNN convolution module for collecting feature data at different time scales and an RNN time-linked module for capturing the changing trend of feature data on the before-and-after time difference and extracting its time-series relationship information. To verify the fit of the CRF prediction model to different time-linked modules, the study added GRU, LSTM, BiLSTM, and other control experiments, and the experimental results on the NASA lithium battery dataset showed that LSTM achieved better results.The main contributions of this work are:Lithium-ion battery residual life prediction is based on the analysis and processing of lithium battery use data to estimate the residual life of the battery. This paper studies how to make the prediction results more accurate and improve the robustness of the model.Since the test time points of each charge and discharge cycle are different, the test number of one cycle at the maximum collection point in the data set is taken as the standard, and the zero vector is used to supplement the insufficient ones. The collected data is first trained through the CNN model of the convolutional window, and then the extracted feature information vector containing the timing relationship is output to the LSTM network for training. After training, a complete implicit state sequence is obtained, namely the vector containing the timing sequence feature information of the charging-discharge cycle. Because the CRF has a good effect on time-series prediction, the vector with time series feature information trained by LSTM is input into CRF model, and the final prediction result is obtained by CRF.The overall framework of the model is shown in Fig. 49series test data. As shown in Eq.  and a memory unit. The LSTM network model is shown in Fig. LSTM uses the forget gate to determine what information can pass through the state unit. The forget gate determines how much information can pass through at the previous time based on the output n in Eq. .4\documeThrough the input gate to generate new information that needs to be updated. This step consists of two parts: the first part is to determine the value for updating Combine the forget gate and input gate, that is, discard redundant information and add new information:The last step is to determine the output of the model. First, get an initial output through the sigmoid layer, and then use Tanh to scale the ct value to −1 to 1, and then multiply the output from the sigmoid to get the output of the model.In the prediction task, LSTM is good at processing long-term series of test data, but it cannot coordinate the dependence between adjacent results of time series data, especially in the face of battery capacity regeneration. CRF can obtain an optimal prediction result through the relationship of neighboring data, and make up for the shortcomings of LSTM. For any sequence A represents the transition score matrix, A represents the score which the predicted value Take the logarithm at both ends to get the likelihood function of the predicted sequence:In the formula, The CRF model is shown in Fig. 48. A set of four Li-ion batteries  were run through 3 different operational profiles  at room temperature. Charging was carried out in a constant current mode at 1.5A until the battery voltage reached 4.2 V and then continued in a constant voltage mode until the charge current dropped to 20 mA. Discharge was carried out at a constant current level of 2A until the battery voltage fell to 2.7 V, 2.5 V, 2.2 V, and 2.5 V for batteries B05, B06, B07, and B18 respectively. Impedance measurement was carried out through an electrochemical impedance spectroscopy frequency sweep from 0.1 Hz to 5 kHz. Repeated charge and discharge cycles result in accelerated aging of the batteries while impedance measurements provide insight into the internal battery parameters that change as aging progresses. This dataset can be used for the prediction of both the remaining charge and remaining useful life.Rom the Fig. The data used in the experiment came from the NASA PCOE lithium-ion battery data setThe data set contains six statistical test features, namely Voltage_Measured, Current_Measured, Temperature_Measured, Current_Load, Voltage_Load and Time.Normalization of data can be used in machine learning algorithms to eliminate the negative effects of different value ranges and to improve the convergence speed and accuracy of the model. The method used in this study is min–max normalization, which scales the data to the interval  through a linear transformation:In order to verify the generalizability of the prediction results of this framework, three sets of data are randomly selected from four battery datasets as the training set and another set as the validation and test set. Figure The experimental code in this paper runs in Python 3.7 environment; the deep learning frameworks are Tensorflow 1.15.2 and Keras 2.2.4; the experiments are implemented on a PC  Core (TM) I9-10900 KF CPU 3.7 GHz, 24 Gbytes of RAM. NVIDIA GeForce RTX 3090 GPU).The correct choice of network model parameters often affects the prediction results. The experiment sets the parameters of step size of the predicted time series, the number of neurons in the network layer, learning rate and batch_size as hyperparameters, and the detailed data are shown in Table 50. PSO completes the search process by the individual search for optimal values and population information sharing, and the Fig. To obtain the hyperparameters suitable for the network model faster, particle swarm optimization (PSO) was experimentally chosen as the parameter optimization algorithm. PSO is a swarm intelligence algorithm for finding optimal parameters, which is often used in the parameter finding the process of network models in battery prediction problemsParameter initialization. Set the number of particles The mean square error of the prediction result is used as the objective function of the particles, and the calculation formula is as follows.Calculate and update the current optimal solution Update the velocity and position information of the particle, and update the formula as follows:Judge whether the loop reaches the termination condition maximum number of iterations In the experiment is divided into the following specific steps:The results of the PSO optimization parameters are shown in Table 51. The calculation methods of each evaluation index are as follows:To quantify the forecast results for comparison and analysis. RMSE and MAPE are used to evaluate the performance of the model in this paperIn the formula, To test the effect of different temporal association modules on the prediction accuracy in CRF models, control experiments of LSTM, GRU, and BiLSTM were designed. The temporal association module compares the difference between using a single-layer CNN network to extract feature information as network input and using a two-layer CNN network. The output of the temporal association module is used as the input of the CRF model, and the experiments are done on B18, and the experimental results are shown in Table The experimental results surface that the prediction error of LSTM is smaller compared with other RNNs, which indicates that the temporal information extracted by LSTM is more adapted to the input with the CRF network. Compared with single-layer CNN networks, two-layer CNN networks can obtain better results. We found that this is because the two-layer CNN changes the length of the input to the network model using the pooling layer compared to the single-layer CNN, which enables the second layer CNN to extract a wider range of feature information.To prove the effect of CRF on the model, the effects of adding CRF and not adding CRF on the CNN-LSTM-CRF predicted results were compared. The comparison results are shown in Table The experimental results found that CRF could improve the accuracy of network model prediction, and the RMSE and MAPE evaluation metrics on four datasets B05, B06, B07, and B18 improved by more than 20% on average compared with no CRF, with the MAPE metric of B06 dataset improving by 53% as the largest improvement of the experiment, which indicated the importance of CRF model, which was also proved on the subsequent experiments of the probability distribution of prediction results.To intuitively reflect the prediction results of this method, Fig. The results from the figure show that most of the predicted results are within the error range of the true capacity. Of course, the predicted values at 78 cycles on the B05, B06, and B07 data sets show poorer prediction results, which is due to the capacity rebound caused by the capacity regeneration phenomenon during the charging and discharging process of Li-ion batteries, and the sudden change in capacity will bring a larger prediction error compared to the smooth state. This is because we use the complete battery dataset for model training to predict a different battery dataset, and the CRF model uses the feature offset matrix during the training process to count the overall trend of the training data and record the overall trend of the battery capacity so that when there is a large error, the error will be reduced in the subsequent prediction process according to the learning record. The error is reduced in the subsequent prediction process based on the learning records.In order to verify the prediction superiority of the CNN-LSTM-CRF model proposed in this paper, comparative experiments were conducted with SVM, LSTM, and GRU models. The RMSE and MAPE results of the four models are compared in Table It can be found from the table that for RMSE and MAPE metrics, the average metrics of the predicted value of the model in this paper are superior to the comparison model, illustrating the feasibility of the CNN-LSTM-CRF model proposed in this paper in the battery capacity prediction problem.For the problem of lithium battery capacity prediction, this paper takes inspiration from the field of NLP and proposes a combined CNN-LSTM-CRF neural network prediction model, which is applied to the battery remaining life prediction for the first time. The model inputs continuous-time battery measurement data and predicts the output battery capacity situation at the current time point to obtain the remaining battery life at this time. Compared with the previous battery capacity prediction network model, the major difference in this model is the inclusion of CRF. The capacity prediction sequence is calculated by the offset matrix of the feature data, which more intuitively reflects the change of the decreasing trend of capacity. The CNN convolutional module is added to the model to collect the feature data, and the time-linked module captures the trend of feature data in the time dimension to extract the temporal information. Among them, LSTM achieves better results in the time-linked module control experiments. The ablation experiments demonstrate the effectiveness of the CRF network in the capacity prediction process. By comparing with previous models, our model achieves better prediction results.Our model still has flaws. The large number of network structures combined makes the network depth and computation of the model huge, which will cost more computational resources and time. Future work can try to experiment with migration learning in the model learning process, and use the extracted trained network parameters to adjust the network model to make the real-time prediction of the model possible.