Dietary intake of protein, fat, carbohydrates, cholesterol, and salt in the intervention group demonstrated significant reduction, while the intake of dietary fiber significantly increased after the dietary education. Simple dietary education of ‘eating vegetables before carbohydrates’ presented by dietitians achieved good glycemic control after a 5-year period in outpatients with T2DM at primary care clinic.The aim of this retrospective cohort study was to evaluate the effect of 5-year follow-up of dietitian-led medical nutrition therapy (eating vegetables before carbohydrates) on glycemic control in outpatients with type 2 diabetes (T2DM) at a primary care clinic. A total of 138 patients with dietitian-led medical nutrition therapy (intervention group) and 104 patients without dietitian-led nutrition therapy (control group) were compared for glycemic control, serum lipid, blood pressure, and diabetic complications for 5 years. Each patient in the intervention group received dietary education focused on food order (eating vegetables before carbohydrates) by dietitians. A significant improvement in HbA1c after 5 years in the intervention group [8.5 ± 1.7% (69 mmol/mol) to 7.6 ± 1.1% (59 mmol/mol),  It is essential to manage good metabolic control in patients with diabetes in order to prevent chronic complications . Meta-anPatients with diabetes who were educated about comprehensive meal plans by registered dietitians have been shown to improve glycemic control and decrease prescribed medication . HoweverRegistered dietitians have a national license and hospitals in Japan, but not necessarily primary care clinics, are required by law to hire registered dietitians. One of the roles of registered dietitians is to provide nutritional education to patients with diabetes, hypertension, hyperlipidemia, malnutrition, etc. The costs of medical nutrition therapy are covered by medical rewards provided by the Japanese government. However, some surveys report that about half of the patients with diabetes have not received diabetic-related knowledge and self-management skills at their primary care clinics and were not provided with the relevant educational programs . One of We reported the significant acute and chronic effects of a simple and easy meal plan of ‘eating vegetables before carbohydrates’ by dietitians on the reduction of postprandial blood glucose concentration in outpatients with type 2 diabetes (T2DM) at primary care clinics ,12, but We recruited diagnosed outpatients who fulfilled the World Health Organization (WHO) criteria for T2DM at the Kajiyama Clinic, which is the primary care clinic for diabetes in Kyoto, Japan. All patients who agreed to participate the study were informed of the importance of the dietitian-led medical nutrition therapy by the doctors at the clinic. The patients were divided into two groups as follows: patients who agreed to receive medical consultation by dietitians were included in the intervention group, while patients who refused to receive medical consultation by dietitians were included in the control group. The reasons for the patients rejecting the dietitian-led nutrition therapy were as follows: time limitation, economic burden, or the patients had already been educated in medical nutrition therapy by dietitians elsewhere. When the patients in the control group changed their mind and wanted to receive the dietitian-led nutrition therapy, they were included in the intervention group.All participants received medical care from a physician every 1 to 2 months. The period of recruitment was from 2004 to 2009, and the 5-year follow-up was performed from 2004 to 2014. The exclusion criteria for participants were as follows: (1) chronic kidney disease stage higher than 4, (2) alcoholic or mental illness, (3) participants who had not received medical care at a clinic for more than 5 years.The medical examination included body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP), hemoglobin A1c (HbA1c), and serum concentration of total cholesterol , low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). The patients in the intervention group and the patients in the control group were compared in glycemic control, body weight, blood pressure, lipid profiles, and diabetic complications. Onset and progress of diabetic complications were assessed within and between the two groups retrospectively. Diabetic complications, including micro-vascular diseases  and macro-vascular complications , were assessed based on clinical status. Diagnoses of nephropathy were based on the Classification of Diabetic Nephropathy  and NeurA total 333 patients agreed to participate in the study, consisting of 196 patients in the intervention group and 137 patients in the control group at baseline . The patIn order to reduce postprandial glucose concentration, a simple and easy dietary method of ‘eating vegetable before carbohydrates’ was taught by the dietitians. We used the original brochure  and figuLaboratory data, body weight, and blood pressure were collected from all patients at baseline and every 1 to 2 months for 5 years at the clinic. Blood pressure was measured twice in the seated position during the physical examination after the patients had rested for 10 min. Blood samples were collected from all patients every 1 to 2 months at the clinic. HbA1c levels were determined by latex cohesion method ; Total-C, TG, and LDL-C levels by enzymatic method .t test. Paired t test or Wilcoxon matched-pairs signed-rank test were used to analyze differences between baseline values and those after medical care. Differences were considered significant at p < 0.05. All analyses were performed with SPSS Statistics ver. 24 software .The primary outcome was HbA1c and the secondary outcomes were blood pressure and serum lipid concentrations. The results are expressed as mean ± SD unless otherwise stated. Baseline characteristics and the outcomes were compared between groups using chi-square, Mann–Whitney U test, or Excluding the patients who failed to keep the 5-year follow-up at the clinic, the results were based on 138 patients in the intervention group and 104 patients in the control group . In thisThe changes in BMI, HbA1c, blood pressure, and lipid profile for 5 years in both groups are shown in In order to avoid the influence of the medication, we analyzed the patients not using insulin or oral hypoglycemic agents (OHA) after 5 years in both groups . HbA1c wp < 0.01), whereas in the corresponding patients in the control group showed no difference in HbA1c after 5 years . Body weight in the intervention group (60.5 ± 12.0 kg to 57.9 ± 13.6 kg p = 0.773) and the control group  demonstrated no change during 5 years in patients with sulfonylurea. Additionally, the patients without use of sulfonylurea in the intervention group demonstrated reduction in HbA1c , whereas for the patients in the control group, HbA1c showed no difference before and after 5 years . As we observed in the patients with sulfonylurea, no change was observed in the body weight in both intervention group  and control group  of patients without use of sulfonylurea.Additionally, we analyzed the effect of sulfonylurea, which was used in more than half of the patients in both groups among the patients using OHA in this study. In the patients with sulfonylurea in the intervention group, HbA1c decreased after 5 years  ,15 mightLarge, randomized controlled studies have documented the effectiveness of nutrition interventions on the medical costs associated with diabetes ,19. The Patients with diabetes encounter several psychological and lifestyle difficulties when modifying their lives to achieve diabetic management, particularly when improving their diet according to medical nutrition therapy ,24,25. TThe approach of eating vegetables before carbohydrates is easier to understand and to attain in making appropriate behavioral changes than other dietary approaches for patients with T2DM ,12,15,26Our study showed important dietary behavioral changes in patients in the intervention group. The patients in the intervention group showed a significant increase in their consumption of vegetables and decreases in grains, sweets, and sweetened beverages after intervention. As a result, the intake of fiber increased, whereas the intake of carbohydrates and total energy decreased. Although this simple and easy nutrition therapy was not aimed at restricting energy and carbohydrate intake, the results showed the therapy was also effective in reducing the intake of energy and carbohydrates. The reason for the reduction in glycated hemoglobin levels in the intervention group can be explained by the decline in the intake of carbohydrates and the increase in the intake of dietary fiber in the vegetables consumed before the carbohydrates. ,37,38,39One of the reasons that the patients in the intervention group could keep their dietary plan long-term may be provided by the increased post-meal satiety and decreased subsequent hunger elicited by the increased consumption of vegetables ,38,39,40Another interesting change was the decline in salt intake in the intervention group. The significant decrease in DBP after dietary intervention may be the result of decreased intake of salt. Because Japanese people usually consume boiled rice mixed with salted side dishes and main dishes , eating There are some limitations which should be considered in this study. First, this study is a retrospective cohort study conducted at a single primary care clinic, and the number of dietary sessions and duration of intervention were not comparable among the patients. Additionally, because the patients in the control group refused to receive the dietitian-led nutrition therapy, the motivation for improving the dietary habits of the patients in the control group might have been insufficient compared to the intervention group. Additionally, the results were obtained from patients who continued receiving medical care at the clinic for 5 years in both groups, and thus, they may be considered as highly motivated patients and might have biased the results, although the ratio of the patients who continued receiving medical care for 5 years at the clinic was lower in the intervention group than that in the control group in the current study (70% in the intervention group vs. 76 % in the control group). Second, we could not compare the dietary intakes between the two groups because we intentionally did not request the dietary intake data from the patients in the control group. The reason for not requesting the data was that we considered that requesting the data itself may evoke consciousness in patients of their dietary habits, and we wanted to exclude the possibility that the consciousness in the patients in the control group may subsequently generate some kind of effect similar to those obtained in the intervention group. Additionally, the patients in the intervention group were interviewed by trained dietitians who were capable of retrieving more accurate dietary intake, which may attenuate errors, but some dietary records reported by patients might have been confounded by recall bias . Third, Our results support the hypothesis that dietitian-led medical nutrition therapy focused on ‘eating vegetables before carbohydrates’ is effective for achieving good glycemic control and delaying further progress of diabetic complications for 5 years in outpatients with T2DM. This dietitian-led medical nutrition therapy of food order supports the concept of emphasizing how to eat, not just what to eat or how much to eat, with restrictions of energy and carbohydrate intake. Additional and longer studies are warranted to investigate the effects of this dietary approach for preventing the diabetic complications associated with T2DM and keeping good quality of life for patients. Thus, the dietitian-led nutrition therapy focused on eating vegetables first before carbohydrates approach may be a preferable approach as the first-line method in patients with T2DM.The dietitian-led medical nutrition therapy of ‘eating vegetables before carbohydrates’ was effective in achieving good glycemic control over a 5-year period with setting individual and realistic goals for one’s glycemic control as well as introducing gradual dietary change according to the patient’s current dietary habits and socioeconomic situations in Japanese outpatients with T2DM at a primary care clinic. At each meal, the consuming order for food of eating vegetables first  followed by the main dish and carbohydrates last was recommended. Strategies for medical nutritional therapy should be re-organized to provide a simple and easy meal method focused on food order with ‘eating vegetables before carbohydrates’, expecting lower long-term glycemic control and the delayed onset and progress of diabetic complications on a lifelong basis in patients with T2DM.


In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans.Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis.TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones. Neurodegenerative disorders have a huge negative impact on the healthcare systems globally. Alzheimer’s Disease (AD) is highly prevalent in the elder population, is considered to be the most common disorder with approximately 60% of all dementia . DementiDifferent neuroimaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are proficient to document the functional and anatomical abnormalities informative to diagnose the type of the neurodegeneration . F-18 flDifferent deep convolutional neural network (CNN) techniques have proven to be effective in supporting the diagnosis of most common types of dementia such as AD, MCI, and DLB using 18F-FDG-PET brain images. These techniques show ability to extract features and identify disease-related patterns in imaging input data without prior-knowledge about the pathophysiological mechanisms of the underlying diseases –9. HowevThere are many CNNs well-trained on ImageNet with differing accuracy. These networks have been trained to recognize objects from a huge natural-image dataset which consists of 14 million images of roughly 1,000 different categories , 13. TheDespite the popularity of TL models in medical imaging, there has been little work studying its precise effects in the medical imaging settings. Particularly, medical tasks often have significantly fewer classes than the standard ImageNet classification. Additionally, TL models are pre-trained with 2D images and thus spatial information is lost during the transformation of the 3D medical images into the 2D space. Furthermore, the nature of features used as representation generated for 3D neuroimaging volumes using pre-trained TL models need further investigation especially with the high resolution non-RGB nature of input data and the low contrast between brain regions and background of scans after data normalization.Given these open issues with lack of scientific studies highlighting the differences among adopting TL models and training specialized models for medical imaging, in this paper the aim is to compare the performance of deep and transfer learning applied to neuroimaging volumes for classification purposes. The goal is to study the performance of these techniques with respect to multiple aspects. The first dimension in our empirical analysis represents the type of classification task, such as binary classification when distinguishing AD from Cognitively Normal (CN) cases, 3-way classification by adding DLB cases, and finally 4-way classification by considering MCI cases. Additionally, we compare performance of these models in terms of training overhead, obtained accuracy, explainability, and sensitivity towards other similar types of dementia.We evaluate the performance of TL models trained with natural images such as ImageNet, as well as custom models for diagnosis of brain disorders using 3D medical imaging data for different classification tasks.We investigate whether using pre-trained TL models lead to different learned representations, by visualizing the generated feature representation by different models. More importantly, we analyze network attention to demonstrate the significant areas of interest indicated by the adopted and developed models. Furthermore, we evaluate robustness and sensitivity of different models towards other similar dementia types.The rest of the paper is organized as follows: in Section Results we present obtained performance from TL and 3D custom model in different experiments, then we provide discussion and related work, lastly we conclude our paper. Section Methods presents the design of comparative analysis performed between the different models.In this paper, we use InceptionV3 , VGG16 , and ResWe start by discussing the dataset and the choice of hyperparameters for the developed 3D models. Then, the results of performance comparison among different models are presented. Lastly, we detail our experiments on network attention for explaining the decisions taken by different models.All of the experiments were conducted using Tensorflow and Keras libraries on a computer with Linux Ubuntu 18.09 operating system, that has one Nvidia Quadro GV100 GPU card with 32GB of memory and 36 CPU core Xenon with 128 GB of memory.Our primary dataset was collected retrospectively from two different sources as detailed in Table The EDLB also provided some normal cases that we added to the CN cases. In total EDLB provided 201 scans from 2005 to 2018. The rest of the images, i.e. 556 scans, were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI)  across ATable We used InceptionV3, VGG16,and ResNet50 models to be evaluated as transfer learning approach which being trained with ImageNet. Furthermore, we trained a 3D VGG model from scratch using our 18F-FDG-PET scans. Table VGG16 was designed for 2D images with small and fixed filters across all the convolution layers  on the training set, also computed the 95% confidence intervals, the results are shown in Table When training a large network, there is a point during training when the model will stop generalizing and start overfitting of the training data. Overfitting results in increasing the generalization error, making the model less useful at making predictions on new data. The challenge is to train the network long enough to make it capable of learning the mapping between inputs and outputs, but not so long to avoid overfitting training data. Particularly, the number of training epochs as a hyperparameter and train the model with an early stopping condition combined with different learning rates and dropout rations. We performed two different experiments to show the effect of different learning strategies , learning rates , and dropout .In the second experiment, we trained 3D-CNN model for 4-way classification tasks for 10 rounds of K-Fold Cross Validation (CV) on the training set. Also, we computed the 95% confidence intervals. Figure For the third experiment, we trained the model with early stopping training strategy with different values of learning rate (lr) and dropout rations as shown in Fig.  Most DL models applied in neurodegenerative diseases mainly focus on binary classification or classify multiple stages of AD from no dementia to moderate AD. However, the utility of such models is limited to the AD patient population solely, which makes them unable to discriminate non-AD patterns from AD, also it becomes hard to validate their robustness in the presence of non-AD disorders. The proper diagnosis of dementia patients requires going beyond binary classification and at least recognizing the differences among Cognitively Normal (CN), MCI and other dementia types, especially the most common ones such as AD and DLB. Therefore, in the following experiment we evaluate the accuracy of used models using different classification tasks.In this experiment we evaluated the performance of different models for different classification tasks. We started with binary classification, where the objective is to recognize AD from CN cases. Then, for 3-way classification we added the DLB cases, and lastly 4-way classification is performed to distinguish among the four classes existing in our dataset. In order to perform this experiment, we randomly divided the training data into 80% used for training and 20% used for validation.For 3D model, we trained our model from scratch using mini-batches of size 6 and Adadelta optimizer with 0.01 learning rate for 50 epochs and Dropout layers with 0.5 rate. Additionally, to prevent the model from overfitting we used early stopping condition as discussed in Experiment 2.For TL models, we trained a separate classifier for each classification task using the features of cases belonging to training set, then computed the validation accuracy with features representing validation set. We repeated this process for 10 rounds and obtained results are listed in Table Additionally, we used normalized mutual information (NMI) index by comparing ground-truth labels with the predicted labels generated by different models. Basically, mutual information index is a non-negative quantity and is upper bounded by the values of entropy of identified classes that can measure the information that predicted and ground-truth labels share. The highest value of NMI can be reached when the predicted labels are exactly the same as the data labels.As shown in Table For classification tasks that consider multiple disorders at the same time, our 3D model performs better than TL models, due to its ability to extract relevant features from input data that make separation boundary among different classes more evident as explained in the results of the next experiment. This also can be shown with achieving highest validation accuracy for 3-way and 4-way classification. Additionally, the values of NMI obtained by our model are the closest to the values computed using ground-truth labels with a prominent difference from values reached by TL models.The difference in performance between specialized 3D model and TL models appears in classifying multiple brain disorders. Therefore, in this section we analyze further the features and classification decisions obtained by different models when the objective is to perform 4-way classification.We used the unsupervised UMAP to visualize 1) the original normalized data and 2) the extracted features by different models (before the classification layer) as shown in Fig.  For TL models, some of DLB cases were separable in the representation space, however, rest of cases belonging to CN, AD, and MCI are overlapping that makes it difficult to reach a decision boundary to separate them while training a classifier. On the other hand, features extracted by 3D model made DLB cases very well separated and it is explaining the good performance of the model. The other interesting pattern in Fig.  Figure Occlusion experiment is used to visualize network towards a specific class. We applied occlusion experiment for ResNet50, InceptionV3 and compare it with results obtained by occlusion on a trained 3D model as explained in Sect. . The results show the cross entropy response of the network given such occluded data as a function of the position of the occlusion box. The experiments were done for all four classes in the training dataset, i.e. when calculating the maps for the DLB class, only DLB subjects were included. The assumption is that when ignoring a relevant region for the correct classification, the cross entropy response will be high.The cross entropy maps are then projected using a mosaic of the slices 5 to 54  is highlighted in both maps and generally both are sharing many common brain regions of interest.Our objective of this experiment is to analyze the sensitivity of models towards similar types of dementia to see how the models recognize these cases and create their equivalent representations. Though, the objective of trained models, as any supervised classification task, implies that the input space is projected into a finite set of defined categories . Yet, in this analysis, we investigate the generated representation for FTLD cases and evaluate the labels assigned to them to see if they are grouped together or scattered across the representation space with labels belonging to multiple classes.Table Figure Deep CNNs require large amount of data for training in order to achieve good classification performance. However, the medical images are hard to obtain, as the collecting and labeling of medical data confronted with both data privacy concerns and the requirement for time-consuming expert explanations. Clearly, there are significantly more datasets of natural images. Thus, TL models have been fine-tuned and applied in medical domain for various classification tasks using their previous learning from ImageNet dataset.Medical images typically represent much higher resolution with non-RGB channels volumes. Additionally, classification models for medical applications need to detect patterns that depend on small and local variations in the input data. Due to this huge diversity between natural and medical image modalities, it remains questionable how much ImageNet feature reuse is helpful for medical images? In this study we focus our analysis on neuroimaging, with detecting diagnosis of most common types of dementia  using 18F-FDG-PET scans of the brain.Designing and training 3D model from scratch can be a challenging task, particularly with limited available data. Too simple models might not be able to learn enough representation of input data, leading to poor performance. On the other hand, training a very complex network with limited data is hard which leads to overfitting. Having a network model with proper size and other effective methods preventing overfitting, such as proper dropout, learning rate, and early stopping, can get the best results. We have demonstrated with different experimentation the effect of these hyperparameters on training 3D model from scratch. Results show that small learning rate leads to a long training process that could get stuck. Furthermore, dropout regularization and early stopping conditions help our shallow 3D model, with only 4 convolutional layers, to avoid overfitting. From a computational-overhead point of view, feature extraction from TL models provides timely efficient solution for binary classification tasks. However, as we show fine-tuning huge pre-trained model  on target medical data can require huge computational resources and take more time than training a smaller custom 3D model from scratch.We have demonstrated with three state-of-the-art ImageNet deep architectures and a well-trained 3D model that a customized 3D model can achieve comparable and even better performance for neuroimaging. In our study, we have successfully demonstrated the effectiveness of the different models in distinguishing neurodegenerative brain disorders with binary, 3-way, and 4-way classification tasks. The results show that TL models obtain superior performance in differentiating AD from CN cases, however, performance decreases when adding more disorders to the classification task. Particularly, performance of 3D custom model becomes better when predicting the diagnosis of two or more brain disorders. We have shown the improvements obtained by 3D model for 4-way classification over TL models using AUC, also by visualizing representation space of extracted features.We have also provided further analysis that goes beyond classification accuracy and demonstrated with occlusion experiments the areas of interest indicated by 3D model, ResNet50, and InceptionV3. The results show that both 3D model and ResNet50 provide heatmaps with specific regions identified per each class. However, TL models were not able to maintain the spatial information among brain regions across the consecutive slices. It is worth mentioning that our results are limited to adopting TL as feature extractors, as we froze the pre-trained weights for convolution layers of TL models and we only fine-tuned fully connected layers to have more specialized classifiers.Pre-training has received much attention in medical image analysis. For example, Nobili et al.  introducDing el al  used IncFew studies shed the light on the limitations of TL in medical imaging. For example, Raghu et al.  show thaCompared to these previous works, our work takes a step forward and studies the effectiveness of adopting TL for different classification tasks. Furthermore, we investigated the generated representation of TL models and compared it with the ones obtained from a custom 3D model. Additionally, we showed using occlusion experiment that the decisions of TL models might not be informative nor related to medical data properties .To understand the benefits and limits of TL and training specialized models for diagnosis of different brain disorders, we not only look at standard performance metrics, but also include analysis of key properties particularly important to training and fine-tuning models, extracted features representing medical data, and network attentions. The design of TL models is likely to be suboptimal for the classification of neurodegenerative diseases. Specifically, we found that supervised TL can indeed lead to superior performance on diagnosis of AD versus CN in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not significantly help performance. Furthermore, custom 3D trained models perform comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. Additionally, the results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.The 18F-FDG-PET scans were spatially normalized to match the International Consortium of Brain Mapping (ICBM space template for European brains) template . SubsequAfter the spatial normalization step for the input scans, the first 10 slices as well as the last 9 slices of each scan were excluded as they contain very small objects. Thus, we have the input data as a 3D volume of The main idea is to treat each 18F-FDG-PET scan separately, then normalize the voxel values using mean and standard deviation. Particularly, we treated each scan as a sequence of 2D images along the axial plane. We applied feature-wise normalization such that each 3D voxel was normalized by subtracting feature-specific mean then dividing by the feature-specific standard deviation per each scan. We performed further scaling to have all intensities values in the range of . To transform the input format from 3D to 2D for TL models, we organized the 60 slices of each normalized scan into a 2D grid resulting in having 2D image with We briefly describe the experiments developed for this study and illustrate the objectives of each experiment. As shown in Fig. Additionally, we designed the fourth experiment to compare efficiency of 3D and TL models in discriminating different neurodegenerative disorders with multiple classification tasks. We evaluate the models by performing binary, 3-way, and 4-way classification. Furthermore, our fifth experiment provides detailed comparison of performing 4-way classification. Besides evaluating accuracy of different models, we investigate the learned representations by visualizing the generated feature representation extracted by different models using a dimensionality reduction technique. We used Uniform Manifold Approximation and Projection (UMAP) to produce 2D features of generated representations of input data using adopted models. We chose UMAP as it preserves the global data structure as well as the relative closeness of data points .During the experiments 1 to 4, we adopted 80% and 20% strategy to split data into training and validation datasets. Regarding experiment 5, as we want to report accuracy using AUC under ROC curves, we needed to use the holdout testing dataset for this purpose, thus we split the data into 90% to be used for training and validation and the remaining 10% is used as an independent testset.We used occlusion to analyze network attention towards significant areas of interest indicated by the models. Occlusion Sensitivity helps to determine whether the output of the model is based on the correct identification of objects with the high sensitivity associated to specific local structures in input images . The occOur last comparative analysis task is dedicated to evaluate the robustness of the models using brain scans of a new dementia type that was not included in training set. For such sensitivity analysis we used eight Frontotemporal lobar degeneration (FTLD) 18F-FDG-PET scans as another external test set for different models. 18F-FDG-PET brain scan of an FTLD patient is expected to have low FDG uptake in the frontal and temporal lobes . A patie


The duration of immunity after first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the extent to which prior immunity prevents reinfection is uncertain and remains an important question within the context of new variants. This is a retrospective population-based matched observational study where we identified the first polymerase chain reaction (PCR) positive of primary SARS-CoV-2 infection case tests between 1 March 2020 and 30 September 2020. Each case was matched by age, sex, upper tier local authority of residence and testing route to one individual testing negative in the same week (controls) by PCR. After a 90-day pre-follow-up period for cases and controls, any subsequent positive tests up to 31 December 2020 and deaths within 28 days of testing positive were identified, this encompassed an essentially vaccine-free period. We used a conditional logistic regression to analyse the results. There were 517 870 individuals in the matched cohort with 2815 reinfection cases and 12 098 first infections. The protective effect of a prior SARS-CoV-2 PCR-positive episode was 78% (odds ratio (OR) 0.22, 0.21–0.23). Protection rose to 82%  after a sensitivity analysis excluded 933 individuals with a first test between March and May and a subsequent positive test between June and September 2020. Amongst individuals testing positive by PCR during follow-up, reinfection cases had 77% lower odds of symptoms at the second episode  and 45% lower odds of dying in the 28 days after reinfection . Prior SARS-CoV-2 infection offered protection against reinfection in this population. There was some evidence that reinfections increased with the alpha variant compared to the wild-type SARS-CoV-2 variant highlighting the importance of continued monitoring as new variants emerge. As of 10 December 2021, 267.9 million coronavirus disease 2019 (COVID-19) cases and 5.3 million associated deaths have been reported globally to the World Health Organisation [The duration of immunity after first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the extent to which prior immunity provides protection against disease or transmission remains uncertain. Potential cases of reinfection have been reported internationally . In EnglAn analysis of a large, multi-centre, prospective cohort study of hospital healthcare workers in the UK found that a prior history of SARS-CoV-2 infection was associated with an 84%  81–87) lower risk of infection during 7 months of follow-up . A retroThe emergence of the alpha , beta 1, gamma , delta  and mostUsing national testing datasets in England we identified people who tested PCR positive for SARS-CoV-2 for the first time between 1 March 2020 and 30 September 2020, as cases. These individuals were matched one-to-one to controls with a negative test result in the same week of test and tested via the same testing route with the same demographic characteristics of age (in years), sex and upper tier local authority of residence as governmental geographical catchment area. We then looked at the risk in both case and control groups of reporting a positive PCR test within a predetermined follow-up period starting 90 days after the last COVID-19 PCR-positive test of the cases until 31 December 2020. This period therefore predated COVID-19 vaccine effects as vaccination began on 8 December 2020 in highest priority groups and anti-spike immunoglobulin G levels mature over the 2–4 weeks after the first dose .The main analysis was a retrospective population-based matched observational study. COVID-19 is a notifiable disease and all laboratories in England are legally obliged to report all SARS-CoV-2 positive test results to Public Health England (PHE) and its successor, the UK Health Security Agency (UKHSA) through the Second Generation Surveillance System (SGSS) . The UniDue to the limited number of paediatric reinfection cases identified in 2020, individuals in England aged 10 years or older were selected based on a first SARS-CoV-2 positive PCR test result between 1 March 2020 and 30 September 2020. This ensured that those selected with a positive test, and those matched with an initial negative PCR result, had at least 90 days to become a reinfection or first infection respectively before the end of the study period on 31 December 2020 and enabled review of reinfection risk prior to widespread introduction of vaccination in England. Upper tier local authority of residence was derived from the patient's postcode or, if this was not available, then from their general practitioner's postcode. Individuals with missing postcodes or with the same postcodes as their testing laboratory were excluded.To be eligible, cases could not test positive for a period of 90 days after the latest positive PCR test from their first COVID-19 episode. Anyone who remained PCR positive for longer than 60 days was excluded from analysis as a possible case of persistent infection. All individuals were linked with the Personal Demographics Service (PDS) which stores demographic information including deaths for all NHS patients . We exclTwo rounds of matching were carried out to match PCR-positive cases to individuals testing PCR negative in the same week with the same sex, age in years, pillar test route  and upper tier local authority . The secWe matched on a month by month basis to exclude matched negatives from becoming part of the study for a second time if they tested negative again after they had already been linked to a case from an earlier month. Both the cases and controls were included if they had previous negative tests prior to inclusion in the study, however neither could have had a prior positive test. Additional details on both cases and controls were extracted including whether they had indicated symptoms at their first test, their ethnicity which was obtained from the National Immunisation Management System (NIMS) and their Indices of Multiple Deprivation (IMD) quintile which was obtained from the individuals' postcode and linked to the 2019 indices of deprivation data; an IMD quintile of 1 represents individuals with postcodes in the most deprived areas.The follow-up period started after the 90-day pre-follow-up period was completed and continued until 31 December 2020. The start of the follow-up period varied depending on when the case first tested positive. Cases that first tested positive on 1 March 2020 had an earliest start date of 30 May 2020 for example, while some cases that first tested positive in September 2020 had their follow-up period start in December 2020.The cases and controls were linked by NHS number and via their unique identifier assigned by SGSS to all records of positive SARS-CoV-2 test results to find any subsequent PCR-positive test results. We used lookup tables to match the controls' unique identifier from the USD to their unique identifier in SGSS. Reinfection cases were identified from the main SGSS dataset through identification of 90-day or longer intervals between sequential positive results for the same individual and these data were linked to our case data via NHS number and unique identifier.We extracted extra information from PCR tests in the follow-up period including whether it was community or hospital-based testing and, for those individuals that were tested through community testing, we also obtained information on whether the participant was symptomatic together with data on the PCR results for the target genes. Three of the four Lighthouse laboratories  used a diagnostic assay that does not detect a deletion of amino acids 69 and 70 of the spike protein, also known as S gene target failure (SGTF). By 15 November 2020, SGTF became a reliable proxy for the alpha (Kent B.1.1.7) variant that emerged in the UK in September and was associated with increased infectivity compared to the wild-type SARS-CoV-2 variant. We identified deaths up to 60 days beyond the end of the follow-up period using PDS records.Controls that subsequently tested positive were excluded from the case population.We used descriptive analysis to describe the population testing positive during the follow-up period. We carried out logistic regression to give the univariable odds ratios (ORs) for testing positive for each of the matching variables, ethnicity and IMD quintile. The upper tier local authorities were grouped into the nine UKHSA regions for the analysis .Table 1Conditional logistic regression was used to take into account the matching design and to give an adjusted OR for the cases and controls testing positive during the follow-up period. We added predictors that were not matched  into the conditional model to test for confounding. As the conditional regression model already accounted for the matched variables, these were not included in the final model.A sensitivity analysis was done by splitting the follow-up time into 30-day groupings to compare the odds of testing positive in the cases compared to the controls during each 30 days of follow-up while excluding those that died or tested positive before the start of each time point. We carried out conditional logistic regression on each 30-day time point while stratifying by month to identify all time-related effects.Secondary outcomes within the population that tested positive during the follow-up period were also monitored. We used logistic regression to compare the odds of dying within 28 days, odds of dying within 60 days, odds of symptomatic infection and odds of second test being S gene negative between cases and controls. We used logistic regression for this analysis instead of conditional logistic regression due to the small numbers of matched pairs where each of the matched pair tested positive. Univariable ORs were calculated for each predictor and then a multivariable model was built. We tested additional variables one by one in the model with the likelihood ratio test. Age categories were regrouped into a binary variable with those aged over 50 years and those aged 10–49 due to small numbers. Region was re-categorised for the S gene analysis into one region with London, South East and East of England and the other category with the rest of England. The first of these regional groupings roughly corresponded to the regions with alpha variant dominance in November and December 2020.After the sensitivity analysis, the dataset was trimmed for a post-hoc conditional logistic regression analysis which excluded 2987 cases and their matched controls that had their first test in either March, April or May 2020 and then had a positive test or died in either the first 90 days , first 60 days  or first 30 days  of follow-up. This corresponds to excluding 933 individuals that tested positive in the summer months between June and September 2020; these cases and controls were excluded due to the stratified results of the conditional regression of the whole dataset and concerns about the quality and limited testing during the early months of the pandemic .Fig. 2.Statistical analysis was carried out in STATA version 15.1, linkage to SGSS and USD datasets was carried out in Microsoft SQL server management studio 18.There were 419 978 individuals that tested positive by PCR for SARS-CoV-2 between March and September 2020 in England. We excluded 22 648 individuals that either had missing demographic information or were under 10 years old, 1911 individuals with persistent infection, 40 983 individuals that died in the pre-follow-up period and 43 408 individuals that could not be found on PDS. The remaining 311 028 individuals with positive PCR tests underwent two rounds of matching to a test-negative with the same sex, age, week of test, upper tier local authority of residence and pillar of testing. A total of 66 827 matched controls were subsequently excluded from the first round of matching as they tested positive before the start of the follow-up period or died during that period. Other reasons for exclusion include failing identification in PDS or being included in the study twice. A second round of matching was undertaken for these individuals . There wThe final cohort consisted of 258 935 successfully matched cases giving a total of 517 870 individuals.During the follow-up period, 14 913 individuals had a positive test; 2815 were reinfections in the cases and the remaining 12 098 positive tests were first infections in the controls. Cases had 78% lower odds of testing positive in the follow-up period than controls . There was a peak in positive tests in the controls at the beginning of the follow-up period and subsequent increase in positive tests around 100 days of follow-up . The disBased on the univariable analysis, men had 32% decreased odds  of testing positive in the follow-up period compared to women , while tThe results of the overall conditional logistic regression which takes into account the matching variables found that cases had 78% lower odds  of a positive test during the follow-up period compared to controls . EthniciOnce the dataset was split into different time points for a sensitivity analysis, viewing the ORs by month of first test revealed that the ORs vary by month of first test and follow-up time  with ORsThe post-hoc analysis excluded 933 individuals that tested positive. Those excluded had a first test during the months of March, April and May and had a positive test during the 90-day, 60-day and 30-day time point, respectively. After additionally excluding the matched case or control for these individuals, the post-hoc dataset for the conditional logistic regression consisted of 516 004 individuals with 258 002 individuals in each matched cohort. After excluding these individuals, the odds of testing positive during the follow-up period for the cases was 82%  lower in cases than for controls.There were 3376 deaths amongst controls and 3031 deaths amongst cases in those that did not have a PCR-positive COVID test during the follow-up period. Of the 13 979 individuals in the post-hoc analysis that tested positive during the follow-up period, 608 controls (5.2%) and 89 cases (3.9%) died within 28 days of the positive test and 703 (6.0%) controls and 111 (4.9%) of cases died within 60 days of the positive test . IndividOf those individuals in the post-hoc dataset that tested positive during the follow-up period, symptom data were recorded for 75% of individuals, and the percentage of missing symptom data varied by age, month of first test, region and first test route (Supplementary Table S1). All tests with symptom data would have all been taken through the community testing route. Of controls that tested positive, 5860 (66.3%) had symptoms; this compares to 499 (30.0%) of the reinfection cases. Men were more likely to report symptoms on their positive test with 1.8 times the odds of having symptoms at their second test compared to women , and all regions of England had lower odds of having symptoms compared to individuals from London. Individuals with Asian or British Asian ethnicity had twice the odds as individuals with White ethnicities to report symptoms  while individuals with Black or Black British ethnicities had 38% lower odds of reporting symptoms . After adjusting for sex, age group, month of first test, region and ethnicity, the odds of cases having symptoms during their reinfection was 77%  lower than for controls having symptoms during their first test.The SGTF was used as a proxy for individuals infected with the alpha variant from 15 November 2020. There were 8435 individuals that tested positive from 15 November, of these 37% had S-gene data available, the percentage of missing S-gene data varied by first test type, region, age, month of first test, first test route and ethnicity (Supplementary Table S2). There were 159 (54.6%) reinfection cases and 1205 (42.5%) controls with SGTF. Individuals that lived outside of London, the South East and East of England had 84% lower odds of having SGTF . Older adults aged above 50 years old had 19% lower odds of SGTF . After adjusting for region, age group, month of first test and ethnicity, reinfection cases had 1.9 times the odds of having SGTF  compared to controls with their first test .Amongst our matched cohort of 517 870 individuals, there were 2815 reinfection cases and 12 098 first infection cases until 31 December 2020. Therefore, individuals with a prior SARS-CoV-2 PCR-positive test had 78% lower odds  of having a second infection compared to individuals with no prior positive, from at least 90 days after their positive test up to a maximum of 9 months later. Protection increased to 82% lower odds  in our post-hoc analysis. These findings demonstrate a baseline of protection from a prior SARS-CoV-2 infection against reinfection before the widespread introduction of vaccination against COVID-19 in December 2020. This finding is in line with the results of a Danish study over a similar timeframe which found 80.5% protection against a second infection amongst 525 339 individuals that were followed in the second wave of the pandemic [Ct) values are obtained from a PCR test and can give an indication of how much viral genetic material there is in a sample, a lower Ct value indicates that there is a higher concentration of virus, higher Ct values mean there is less virus and could indicate a persistent infection. However, Ct values are not recorded for all COVID-19 PCR tests in England and Ct values cannot be compared between different assays [The sensitivity analysis further investigated the odds of testing positive at different time points by month of first test thus revealing that there were higher odds of testing positive in the cases compared to the controls within individuals that had their first test in March, April and May and then had a positive test during the follow-up period in June, July and August and September. The reasons for this disparity and high number of reinfections compared to the overall protective effect of prior SARS-CoV-2 infection are uncertain; however, it is possible that the population who were tested during the beginning of the pandemic was fundamentally different and was more likely to be elderly, hospitalised patients due to limited testing. Indeed 50.1% of individuals with a first test in March, April or May were aged over 50 years compared to 27.1% of individuals with a first test in June–September and 59.1% of individuals tested in these early months were tested through hospital testing compared to only 12.6% of individuals with first test in June–September. The uncertain reinfection cases occurred soon after the end of the 90-day pre-follow-up period and so this could mean that some are persistent infections in this specific population. Cycle threshold  compared to the controls, although these results should be interpreted with caution as we used SGTF as a proxy for the alpha variant and there was a high proportion of missing SGTF data. The delta variant emerged in spring 2021 and contributed to over 60% of sequenced cases in England by 17 May 2021 . This waAs COVID-19 vaccine roll-out continues and some countries have started delivering booster programmes, it has become increasingly important to understand the impact of previous infection on the efficacy of COVID-19 vaccines and the need for boosting in a two-dose regimen. The COVID-19 vaccines currently in use trigger immune responses to the spike protein; however natural infection generates a broader humoral and cellular immune response with convalescent individuals demonstrating strong CD8+ T cell responses . The datAs the number of individuals with previous SARS-CoV-2 infection increases, we will continue to see more reinfection cases and therefore it is vital that we continue to monitor the duration and nature of protection against reinfection in populations with high vaccine coverage and different emerging variants.