Ovarian cancer stands as the fifth most prevalent cancer among women, causing more mortalities than any other disease of the female reproductive system. There are numerous histological subtypes of ovarian cancer, each of which has distinct clinical characteristics, risk factors, cell origins, molecular compositions, and therapeutic options. Typically, it is identified at a late stage, and there is no efficient screening method. Standard therapies for newly diagnosed cancer are cytoreductive surgery and platinum-based chemotherapy. The difficulties of traditional therapeutic procedures encourage researchers to search for other approaches, such as nanotechnology. Due to the unique characteristics of matter at the nanoscale, nanomedicine has emerged as a potent tool for creating novel drug carriers that are more effective and have fewer adverse effects than traditional treatments. Nanocarriers including liposomes, dendrimers, polymer nanoparticles, and polymer micelles have unique properties in surface chemistry, morphology, and mechanism of action that can distinguish between malignant and normal cells, paving the way for targeted drug delivery. In contrast to their non-functionalized counterparts, the development of functionalized nano-formulations with specific ligands permits selective targeting of ovarian cancers and ultimately increases the therapeutic potential. This review focuses on the application of various nanomaterials to the treatment and diagnosis of ovarian cancer, their advantages over conventional treatment methods, and the effective role of controlled drug delivery systems in the therapy of ovarian cancer. Ovarian cancer (OC) is a prevalent female reproductive organ malignant tumor that is frequently detected at an advanced stage and is prone to spreading to the pelvic and abdominal cavities, causing malignant ascites . Once rein vitro and in vivo  administration of active drugs containing platinum, such as cisplatin and paclitaxel. Clinical trials comparing IP cisplatin to IV cisplatin were regulated by the Gynecology Oncology group. According to the findings of the following study, intraperitoneal (IP) cisplatin therapy can prolong intravenous (IV) chemotherapy for a longer period . General.OC does not typically exhibit any clinical symptoms in its early stages, which makes it difficult to diagnose this tumor. The symptoms of OC in its early stages are commonly misdiagnosed as those of other benign pathologies, including urogenital infections, gastrointestinal disorders, and benign ovarian lesions  . The utiin vivo imaging, intracellular transport trafficking, and other critical mechanisms (The Food and Drug Administration (FDA) of the United States has previously approved a variety of nanoparticle-based medications for targeted cancer therapy. For instance, the FDA has approved the use of Onivyde, a PEGylated Liposomal Carrier for Irinotecan, to treat Metastatic Pancreatic Adenocarcinoma. Irinotecan, which inhibits topoisomerase I, was found to accumulate in tumors, where it could be released slowly and exert its anti-tumor effects for a longer period of time . Similarchanisms . In a cuchanisms . BecauseScreening for OC currently available in clinical practice is limited to clinician-performed physical examinations, transvaginal ultrasound (TVU) imaging of the adnexa, and measurements of the protein biomarker cancer antigen 125 (CA125) levels in the serum. The most frequent imaging technique used to find OC is TVU, which enables medical professionals to spot changes in ovarian tissues' size and shape. Radiologists examine transvaginal ultrasound images to look for certain clinical features in accordance with the International Ovarian Tumor Analysis’s simple rules. Characteristics such as ascites, papillary projections, and internal blood flow are evaluated to make predictions about the presence and development stage of malignant masses .The most common OC biomarker, CA125, is a glycoprotein found on the surface of epithelial cells and is thought to encourage the growth and metastasis of cancer cells. 80% of OC patients were found to have elevated serum levels (>35 U/mL) of CA125 . UnfortuCancer antigen 19-9 (CA19-9) and human epididymis protein 4 (HE4) are two additional single biomarkers that have recently drawn attention due to their increased expression in less prevalent subtypes of OC. In contrast to mucinous or clear-cell tumors, endometroid and serous ovarian tumors have been reported to be associated with increased secretion of HE4, which is a surface glycoprotein similar to CA125 . AccordiIn vitro diagnosis (IVD) of OC-related biomarkers has become a significant advancement in the diagnosis of OC. Emerging biomarkers  and classic biomarkers (HE4 and CA125) show considerable potential. The detection performance of these biomarkers is enhanced by a variety of superior detection technologies, including optical biosensors, microfluidic chips, and electrochemical biosensors, in combination with nanomaterials, including carbon nanomaterials, quantum dots (QDs), polymer materials, and metal NPs  , which prolongs the half-life of therapeutic agents and encourages passive and active tumor targeting, can all be loaded onto nanocarriers through chemical conjugations or physical adsorption . Exampletal NPs   as a support material for molecularly imprinted polymers on the surface, the use of nickel nanoclusters (Ni NCs) and cadmium (Cd) NCs as efficient and cost-effective emitters have also been introduced. The proposed approach for the clinical diagnosis of CA 15-3 and CA 125 tumor markers had excellent characteristics, including a good reproducibility, broad linear range, and ease of operation. In conclusion, optical biosensors were developed through a simple preparation procedure, and they demonstrated quick analysis times and good stability. These findings show enormous potential for the evaluation of CA-125 in real samples and pathological diagnosis, cell lines, and promise as an approachable assay for enhancing point-of-care analysis.For the detection of multiple cancer biomarkers in specimens from cancer patients and the detection of specific biomarkers In biomedical research or biochemical processes, a liquid droplet’s motion is frequently controlled by microfluidic and nanofluidic devices. The micro/nanofluidic platforms have numerous benefits such as efficient fluid processing, miniaturization, simple integration, rapid analysis, and low sample consumption, therefore offers extensive opportunities for cells analysis . Usuallyvia liquid biopsy such as ascites in women with OC, for rapid assessment of the development of metastatic disease and patient-specific treatment determination.Dissociating exosomes from OC serum has been reported using an antibody-functionalized microfluidic framework to identify biomarkers present in cancer exosome membranes . After being significantly released, the intact exosomes eventually internalized in OC cells . To extrContrast agents utilized in MRI/CT scans to identify tumors have disadvantages such as toxicity, short retention time, and short imaging time. Imaging with multiple spectral photoacoustics enables spectrally enhanced images that penetrate deeper than conventional contrast agents. In a recent study, the cargo release kinetics of V7-NPs, which were created from mesoporous silica NPs with wormhole pores (V7-RUBY) and loaded with an imaging dye (IR780), were assessed. Both proximal tubule kidney cells and non-malignant hepatocytes were evaluated for V7-RUBY’s toxicity. Utilizing the ovarian tumor’s acidic pH-controlled environment, chitosan was able to regulate slow cargo release from V7-RUBY . A shortIn vivo imaging of subcutaneous xenograft models of ovarian tumors was successful using gold nanorods that combined the SERS and PA imaging modalities as shown in 3O4) as T2 MRI contrast agents has increased recently. Few studies, however, have investigated their potential as OC contrast agents. One such study found that branched PEI-conjugated Fe3O4 NPs prepared via a reduction route and targeted to folic acid were effective in identifying ovarian tumors that were situated in the nude mice abdominal cavity (Surface-enhanced Raman spectroscopy (SERS) is a unique characteristic of noble metal NPs because of their inherent localized surface plasmon resonance. l cavity .Short laser pulses are produced in the near-infrared region by non-linear optical imaging using two-photon intense excitation, which results in improved imaging. Due to the development of the fluorescence signal close to the fluorescence spot, this imaging method eliminates the need for a background-free signal and prevents photodamage to living tissue, resulting in higher axial resolution . ResearcThe standard first-line chemotherapeutic regimen of taxane and platinum-based medications, such as cisplatin or carboplatin with paclitaxel, is used as the primary treatment for OC following cytoreductive surgery . Howevervia nanocarriers offers numerous benefits. These benefits include the following: 1) the delivery of drugs that have a low solubility by either enclosing the drugs within the nanocarriers' hydrophobic interfaces or acting as the carriers for the drugs in the blood; 2) lowering chemotherapeutic agents' systemic toxicity; 3) stabilizing their cargo by increasing the biodistribution and pharmacokinetics of the therapeutic agents while reducing renal clearance and extending circulation time owing to drug encapsulation and protection against inactivation by metabolic enzymes; and 4) overcoming drug resistance by selectively targeting cancer cells during nanocarrier uptake via cellular endocytic pathways and simultaneously multiple chemotherapeutic agents are delivered to the tumor while evading the cellular drug efflux pump  could be used in combination to further surmount OC chemoresistance. Li et al., has recently reported an exosome/liposome hybrid nanoparticle system for the co-delivery of miR497 and TP to overcomes chemoresistant ovarian cancer as shown in in vitro and therapeutic efficacy in vivo , dephosphorylated the overactive PI3K/AKT/mTOR signaling pathway, and upregulated macrophage polarization from M2 to M1 macrophages. It was determined that the results offered a translational approach to treating cisplatin-resistant OC and might provide a treatment option for other cisplatin-resistant tumors.When liposomes are functionalized with a ligand, they can target different types of tumor cells and may improve the effectiveness of drug delivery to cancer cells. The liposome with ligand on its surface makes it easier for the active drug to target receptor sites and to directly uptake the drug into tumor cells without having any negative effects on normal cells . Doxorub in vivo . Using ain vivo, result in a rapid recovery with a smaller scar size and minimal pain for the patients, while retaining a higher capacity and a more efficient drug or gene encapsulation for their delivery. Injectable hydrogels that undergo in situ gelation are superior for targeted and localized drug delivery inside tumor cells because they can both keep their contents safe inside the tumor and incisively releases the drugs into cancerous cells  network called a hydrogel. These materials show great promise for a wide range of biomedical applications because they are simple to fabricate, biocompatible, can be tuned to achieve the desired composition, and have superior physical properties. The main goal of hydrogel-based technology is the creation of injectable hydrogels, in which the gel precursor, typically aqueous, is combined with other biologically active substances or biopolymers before being injected into the desired area of interest using a syringe . The prius cells .Due to its essential function in progression and tumorigenesis, the salt-inducible kinase 2 (SIK2) is a highly attractive therapeutic target for OC. The clinical uses of SIK2 inhibitors are restricted due to the potential for significant off-target effects when administered directly. To deliver cisplatin to OC cells specifically and effectively and to enhance the drug’s anti-cancer effects, and prevent cisplatin resistance, Polymeric micelles are nanostructures that resemble the morphology of dendrites and liposomes. The outer layer is typically composed of hydrophilic polymers, while the inner core contains drugs that are poorly soluble in water and is hydrophobic. The polymeric micellar’s outer shell serves as the structure’s barrier against interactions with the reticuloendothelial system and blood constituents. The hydrophilic exterior can be modified by polyethers such as poly (ethylene oxide) and PEG. Covalent bonding, hydrogen bonding, π-π interactions, electrostatic bonding, and hydrophobic interactions are some of the cohesive interactions between the micelle’s inner core and the entrapped drug .in vivo stability -loaded lipid nanoparticles (TPLN) for improving the chemotherapy response of cancer cells. The nanoparticles system was prepared by dissolving the drug (PTX), cholesterol and transferrin conjugated lipid (Tf-PEG-OA) in organic solvent and then evaporated the solvent through evaporation as shown in Solid-lipid nanoparticles (SLNs) are spherical colloidal nanocarriers composed of lipids, surfactants, and chemotherapeutics in an appropriate ratio. Their average diameter is between 50 and 1,000 nm. SLNs are superior to currently available polymeric nanoparticle-based drug carriers due to their nano size and lipid core. Due to their long duration of circulation, notable biocompatibility, and superior tumor accumulation as a result of the EPR effect, lipid-based nanostructures have attracted considerable interest as drug nanocarriers . To prodvia electrostatic attraction, adsorption and infiltration  make them suitable for targeting corresponding receptors on cancer cells. in-vitro epithelial HeLa ovarian cells and demonstrated significantly increased cellular uptake and anticancer efficacy without toxicity. Chen and colleagues synthesized selenium/ruthenium NPs loaded with RNAs (siRNAs) that inhibited taxol-resistant MCF-7/T cells microtubule dynamics  agents  agents . AuNPs s) agents . The forOC cells . Huang aOC cells . Magnetiin vitro and in vivo efficacy, and stability are some of the factors that need to be taken into account, along with their physicochemical characteristics, materials, and loaded cargo.Due to the effectiveness of nanotechnology based drugs delivery vehicles, it is gaining much attention for effective cancer treatment. In this review, the currently investigated nanocarriers for controlled drug delivery are discussed as a developing strategy in cancer therapy, along with the need for the development of such strategies in terms of targeted therapy. Several nanocarriers, including polymeric systems, polymeric micelles, liposomes, inorganic NPs, and hydrogels have been thoroughly investigated for the delivery of various chemotherapeutics for various cancer treatments. As a result, an emerging strategy for the treatment of OC in the future could involve the administration of multiple chemotherapeutic drugs in a suitable nanocarrier, either in combination or individually. Nanocarriers' safety, administration route, ease of delivery, in vitro and in vivo. However, there are currently only a small number of nanocarrier-based drugs that have been approved for clinical use. Regulatory issues, safety concerns, nanomedicines' physicochemical characterizations , and manufacturing problems are some of the obstacles that must be overcome in the clinical application of these nanomedicines. In addition, research that is based on polymers will undoubtedly continue to thrive, and scientists have to focus their efforts on further designing and modifying polymers to address photobleaching issues and a short blood circulation life in practical applications.One of the most advanced and encouraging methods of treating OC is nanomedicine. Numerous studies have indicated that the use of nanomedicine therapeutics for the treatment of OC is highly effective both in vivo effectiveness of chemotherapy and make it more cost-effective.Future advances at the interface of physical sciences and engineering will contribute to the development of innovative approaches that will provide patients and physicians with the information, therapeutics, and diagnostics necessary to eradicate diseases, such as OC. Over the past 5 decades, the combined effects of the fields of engineering, physical sciences, and oncology has proven to be an effective strategy for the treatment of OC, resulting in a technological and medical revolution. The integration of these fields also has the potential to speed up the diagnosis of OC at an extremely early stage, which will prevent the need for costly, invasive treatments for metastatic cancer at a late stage. The effectiveness of any treatment can be further increased by employing synergistic strategies such as immunotherapy and chemotherapy to boost immune system to fight cancer. Nanotechnology based devices and implants can be directly placed in tumor sites through minimally invasive surgery that will increase the


Major violations in BTV determination before radiotherapy were observed in 17% of patients and in 11% of patients in image interpretation at the time of suspected tumour relapse. The interrater variability reported in the study illustrates that the assessment of quantitative parameters derived from amino acid PET may be a significant source of error when conducting clinical trials in multi-centre studies. Consequently, careful training seems necessary even for experienced Nuclear Medicine physicians; alternatively, fully automated segmentation tools might aid towards more reproducible results F-FET will become a more generally used and accepted tracer validation across centres, harmonization of data analyses comparable to the concepts on [18F]F-FDG should be considered.It needs to be considered, however, that especially the parameter TBRage data . Therefo18F]F-FET PET is predominantly based on the parameters TBRmax and TBRmean, which show less variability between different centres; therefore, it might be worthwhile to consider rather these parameters in future multi-centric settings.In summary, the evaluation of BTV seems to be subject to high variability at different centres and requires careful training. In clinical practice, the evaluation of [


In the last decade, there has been an increased appreciation for mitochondria as central hubs in diverse processes, such as cellular energy, immunity, and signal transduction. As such, we have become aware that mitochondrial dysfunction underlies many diseases, including primary  and secondary mitochondrial diseases , as well as complex diseases with mitochondrial dysfunction (chronic or degenerative diseases). Evidence suggests that mitochondrial dysfunction may often precede other pathological signs in these disorders, further modulated by genetics, environment, and lifestyle. Therefore, it is critical to understand the role of mitochondrial dysfunction in certain diseases to comprehend the impact of mitochondrial disorders on developing neurological diseases and how mitochondrial medicine can treat chronic diseases.This Collection aimed to gather research works providing insights into the mechanisms of bioenergetic deficits and potential therapeutic options. Our understanding of the role of mitochondrial dysfunction (MD) in disease has rapidly expanded in recent yearsAmong the excellent studies in this Collection, we would like to highlight a few that address some of the gaps in knowledge mentioned earlier.6 reported high human chorionic gonadotropin (hCG) expression in placentae from pregnancies associated with fetal growth restriction with and without pre-eclampsia. This over-expression could result from HIF-1ɑ stabilization sustained by high oxidative stress because of syncytiotrophoblasts' low mitochondrial antioxidant capacity during the first eight weeks of pregnancy. The high levels of hCG result in deficits in mtDNA translation, leading to decreased bioenergetic capacity6, possibly contributing to fetal growth restriction. While this study addresses the mechanism of MD in pregnancies with relatively high hCG, more research must address the detrimental effect of abnormally low and high hCG levels, as both are generally associated with an increased risk of adverse pregnancy outcomes8.In the context of mechanisms of prenatal adverse outcomes and MD, several studies pointed to the associations between serum-free beta chorionic gonadotropin and adverse maternal, fetal, and pregnancy effects . Kiyokoba et al.9 showed that N-oleoyl-ethanolamide treatment significantly improved mitochondrial morphology and function of BTHS lymphoblasts, possibly by affecting mitochondrial dynamics without resolving cardiolipin alterations. The other one10 reported that SS-31 peptide improved mitochondrial respiratory capacity and promoted supercomplex organization in a BTHS animal model without affecting altered cardiolipin remodeling. Both studies showed a future therapeutic strategy that does not necessarily involve correcting cardiolipin remodeling deficits in BTHS.Regarding treatments targeting mitochondrial dysfunction, three studies explored therapies to improve mitochondrial function. Two focused on Barth syndrome (BTHS), a congenital disease associated with early onset cardio-skeletal myopathy caused by mutations in the TAZ gene encoding the cardiolipin remodeling enzyme. Surprisingly, both studies provided new evidence to support that cardiolipin remodeling may not be required to improve mitochondrial function in this syndrome. One study11. Thus, targeting TDP-43 toxicity seems essential for finding therapies for these diseases when inclusion bodies are evident11. In this context, Gautam et al.12 reported that MD occurs early in ALS, as in other neurodegenerative diseases. Restoring NAD+ levels with nicotinamide mononucleotide improved the neurite length and mitochondrial cristae structure of ALS-affected murine corticospinal motor neurons. This therapeutical approach could become a new strategy that, if implemented early in ALS, may prevent the formation of inclusion bodies and the progression of the disease.Another study reported the importance of early mitochondrial metabolic defects and a therapeutic strategy to correct them in amyotrophic lateral sclerosis (ALS). TDP-43 is a versatile RNA/DNA binding protein involved in RNA-related metabolism. Hyper-phosphorylated and ubiquitinated TDP-43 deposits act as inclusion bodies in the brain and spinal cord of patients with motor neuron diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration. Treatments for ALS are challenging because of the complexity of the disorder, which involves numerous mechanisms linked with progressive motor neuron degeneration, including glutamate-mediated excitotoxicity, protein aggregation, increased oxidative stress, endoplasmic reticulum stress, MD, neuroinflammation, and gene expression dysregulation16. In this Collection, Giulivi et al.17 demonstrated the feasibility of an artificial neural network (ANN), based on mitochondrial bioenergetics and brain imaging, in predicting the progression of the neurodegenerative disorder fragile X-associated tremor/ataxia syndrome (FXTAS). Utilizing brain MRI data from patients and mitochondrial bioenergetic information from peripheral blood mononuclear cells and fibroblasts, ANN was able to make a suitable stage classification for FXTAS. If the ANN approach and outcomes are validated in larger cohorts, the predicted trajectories through combined analyses of cellular mitochondrial bioenergetics and brain imaging could represent a breakthrough in clinical screening and detection of FXTAS morbidity as well as other neurogenerative diseases.In pursuing effective therapies for neurodegenerative diseases, recent findings suggest that addressing metabolic dysfunction early in the disease may also hold promise for preventing overt symptoms or slowing the disease progression. Alongside these efforts, advances in AI technology offer exciting possibilities for enhancing disease diagnosis and treatment. Artificial intelligence (AI) techniques, such as convolutional neural networks, knowledge graphs, and transformers, can revolutionize our understanding and management of various medical conditions, including neurodegenerative diseasesIn summary, the studies published in this Collection demonstrate how far our understanding of the pathogenic mechanisms, disease diagnosis, and management of affected individuals with mitochondrial disorders and conditions with mitochondria deficits has come in the last 5 years. However, these studies also highlight the work that needs to be done to improve our understanding and treatment of these disorders. Nevertheless, mitochondrial dysfunction represents a major mechanistic basis, predictive biomarker, and therapeutic target of various genetic, environmental, or age-associated complex diseases.


Editorial on the Research TopicGut permeability-related endotoxemia and cardiovascular disease: A new clinical challengeγ and tumour necrosis factor alpha (Emerging evidence indicates that gut dysbiosis is a risk factor for cardiovascular disease (CVD) in type 1 diabetes mellitus  or hyperor alpha , increasor alpha . Low, noor alpha . LPS levor alpha .The human body encompasses local and systemic mechanisms to counteract LPS translocation or to detoxify it. Local defence mechanisms include the intestinal mucus that separates gut microbiota from the intestinal barrier consisting of epithelial cells, that are held together by adhesion proteins such as tight junction (TJ) proteins, adherence junctions proteins (cadherins and catenins), gap junction (connexin) proteins and desmosomes (desmoglein and desmocollins). Just below the epithelial barrier, the gut vascular barrier is also implicated in modulating bacteria translocation into the portal vein . Two seqconditio sine qua non to observe increased LPS translocation into the systemic circulation with ensuing endotoxemia  the mechanism by which gut microbiota metabolites, such as trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFA) or LPS, influence the progression of CHD, (2) microbiota-targeted therapy in CHD management and (3) dietary modification as a possible tool to decelerate the progression of CHD. Moreover, they predict 2 research themes that will play a significant role in the future including the research about novel intervention strategies with natural compounds to modulate gut microbiota and its metabolites in patients with CHD and the study of specific mechanisms of action of gut microbiota affecting CHD.Articles published in this Research Topic reported interesting findings and helped with our further knowledge on this topic. Thus, Sun et al., the authors discussed the role of various statins affecting the gut microbiota and LDL-C metabolism through the gut-liver axis. After oral administration, statins enter the intestine via the upper gastrointestinal tract and affect the abundance of gut microbiota, which in turn modulates microbiota-derived metabolites such as SCFAs, secondary bile acids (SBAs), TMA, and LPS. These metabolites, transported to the liver through the portal venous system, can regulate cholesterol metabolism and LDL-C levels by several signaling pathways in hepatocytes. Even if preclinical studies have shown the positive effect of statins regulating LDL-C by gut-liver axis, limited studies are reported for humans.In the review by Vreeken et al. described the role of endothelial PLXNA4, a protein that results downregulated in inflammatory conditions. The authors demonstrated that inflammatory stimuli reduce endothelial PLXNA4 expression resulting in rearranged cytoskeletal structures and cell-cell junctions. These structural changes increased vascular permeability to inflammatory cells, such as monocytes, into the arterial wall favouring atherosclerosis.Inflammation promotes every stage of atherosclerosis, from initiation to progression and evolution. Zhang et al. aimed to verify the association between the serum levels of alkaline phosphatase (ALP) and stroke in a cohort of adults with hypertension. They found that hypertensive adults with higher serum ALP had a significantly higher risk of first total stroke.Finally, FV: writing—review, and supervision. CN: writing and editing. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the past, indications for total pancreatectomy (TP) were rare, with several concerns about patients’ postoperative quality of life due to exocrine and endocrine post-pancreatectomy management .Recently, the number of patients undergoing TP has increased. Previously, TP was performed only in patients with chronic pancreatitis or for intraoperative findings of persistent cancer infiltration at intraoperative frozen sections. Currently, these classic indications have been enlarged by TPs performed for the treatment of intraductal papillary mucinous neoplasm (IPMN)  or for pIn addition, postoperative pancreatic fistula represents the Achilles’ heel of pancreatic surgery ; TPs havAlthough numerous articles have reported on the positive quality of life that patients can achieve after TP , some doIn order to safely propose patients for TP, it is also mandatory for the surgical community to be updated on newly available glycemic control methods.The primary aim of diabetes management is to achieve optimal glycemic control in the long term in order to prevent or delay diabetes-related complications. Although intensive insulin therapy represents the gold standard regimen for subjects with insulin deficiency , this apHowever, during recent decades, diabetes technology has rapidly improved, particularly in the areas of insulin pumps and continuous glucose monitoring (CGM), reducing blood glucose fluctuations in daily living activities and ameliorating a perceived quality of life .Continuous subcutaneous insulin infusion (CSII) systems are programmable pumps that continuously deliver rapid-acting insulin into the subcutaneous tissue. Compared to multiple daily injections (MDI), CSII improves flexibility in terms of prandial bolusing and physical activity while also improving glycemic control during the early morning and fasting, illness, and stressful periods.On the other hand, the CGM system consists of a subcutaneous sensor that measures the glucose concentration continuously in the interstitial fluid, providing near real-time glucose measurements. This approach allows subjects to monitor their glucose levels and trend, demonstrating beneficial qualities in both type 1 and type 2 diabetes irrespective of treatment regimens.Novel combined systems have been launched in recent decades with to integrate CSII and CGM technologies. The first integrated systems  were able to automatically and temporarily stop insulin delivery when blood glucose levels reached the hypoglycemia threshold (<70 mg/dL). Soon after, the Predictive Low Glucose Suspend system (PLGS) went beyond LGS, improving this technology by predicting hypoglycaemia and suspending insulin delivery before this adverse event could occur. Although both LGS and PLGS have proven to reduce the frequency and severity of hypoglycaemia, these systems did not help the prevention of hyperglycemic spikes.Afterward, more complex glucose-responsive insulin delivery algorithms were developed, to automatically address both hypoglycaemia and hyperglycaemia, promoting appropriate glycemic control during the day. Hybrid closed loops (HCL) and advanced hybrid closed loops (AHCL) are technology-advanced insulin pumps characterized by the coexistence of algorithm-driven automated insulin delivery, based on glucose sensor values, with manual mealtime boluses. These technologies consist of three technologies: a CGM sensor that transmits continuous glycaemic values to an algorithm; an algorithm that analyzes CGM data and calculates the insulin required to prevent hyperglycemic and hypoglycemic events; CSII that delivers insulin according to an algorithm .HCL and AHCL systems, known as “artificial pancreas”, modulate insulin delivery continuously and deliver adjunctive small, automated correction boluses when glucose is predicted to rise above the range (AHCL). To date, three main types of closed-loop control algorithms have been used in clinical practice.The first algorithm was the proportional, integral, derivative (PID) controller. This is an algorithm that treats by directing targeting insulin doses based on the difference between the target glucose at the current point in time , the rate of variation in glycemic values over time (derivative), and the area under the curve between the glycemia value detected and the glucose levels to be reached  . The secLastly, the Fuzzy logic approach regulates insulin infusion rates by applying rules derived from the clinical practice of diabetologists and experts. Furthermore, it is able to suspend insulin delivery when glucose is low or is expected to decrease disproportionately.Several studies have compared novel artificial pancreas systems and conventional insulin therapy. MPC-based artificial pancreas systems have been shown to improve the percentage of time spent in the euglycemic range better than both traditional insulin therapy and the sensor-augmented pump ,14. A stHowever, irrespective of the algorithm used, HCL and AHCL have been shown to improve glycemic control, increasing time in the glucose range (70–180 mg/dL) while simultaneously decreasing the rates of hypoglycemic events , especiaBecause of these considerations, all current commercially available devices require the subject to enter the grams of carbohydrate they have ingested, receiving advice on the “recommended bolus” based on a pre-set prandial insulin-to-carbohydrate ratio and insulin sensitivity factor. However, a recent randomized controlled trial published on Diabetes Care evaluated a fully automated closed-loop control system in the absence of carbohydrate intake announcement, showing an improved time in range (TIR) following an unannounced meal for the algorithm called RocketAP (RCKT) and a novel MPC system with a dedicated bolus priming system to address meal-like disturbances compared with a legacy closed-loop system .In conclusion, the development of new technologies applied to diabetes care allowed a significant improvement in the glycaemic control of people with insulin-dependent diabetes, also reducing the risk of hypoglycemia . By redu


There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists. Artificial intelligence (AI), including its subsets of machine learning (ML) and deep learning (DL), is currently one of the most heavily researched fields in radiology, including neuroradiology. The results of a recent PubMed database search for “artificial intelligence” and “neuroradiology” revealed that more papers were published in 2021 and 2022 than all years prior . In factThis paper will provide an overview of the current topics and advancements in artificial intelligence related to the field of neuroradiology, including ischemic stroke, intracranial hemorrhage, brain tumors, neurocognitive imaging, white matter lesions, spinal imaging, and head and neck imaging. It will primarily focus on the performance of new and innovative research tied to current or recent AI-based challenge competitions, with particular attention paid to tasks that are central to neuroradiologists and with an emphasis on the literature published in 2017 or later.https://grand-challenge.org/, accessed on 15 May 2023). Radiology AI challenge competitions often make use of datasets of medical images of a given modality; for neuroradiology, the most commonly used radiologic modalities are computed tomography (CT) and magnetic resonance (MR) imaging, although radiography and positron emission tomography are occasionally included. In most cases, the datasets consist of images obtained via standard clinical protocols for processing and may also include metadata related to image acquisition parameters or other demographic or clinical data. Teams that submit candidate algorithms are typically permitted to perform any additional image processing steps they deem appropriate, such as masking or scaling. The design of these competitions varies depending on the scope and goals of each competition. Typically, candidate algorithms are expected to perform a specific computer vision task, such as lesion detection, segmentation, or classification, and public training datasets are made available to participants for this purpose; their performance is evaluated using a test set that is not made public during the competition.AI challenge competitions are events organized around a specific task or problem. These competitions are designed to promote the research and development of AI by providing a platform for testing various algorithms and models against current best practices and other competing AI methodologies. They provide unique opportunities for researchers and vendors to benchmark their AI-based algorithms. Many current AI biomedical imaging challenges can be found on the website Grand Challenge  is a subtype of AI and refers to a technique by which programs learn patterns and/or adapt based on experience, typically through the use of large training datasets. ML can be categorized into supervised, unsupervised, and semi-supervised versions. In supervised learning, the datasets are labeled by an expert to “train” the algorithm to produce a desired output, with each piece of data refining the algorithm further. This is one of the primary AI methods used in radiologic imaging. In unsupervised learning, the datasets are not labeled. Patterns within the dataset are inferred by the AI algorithm without prior information or guidance. As its name suggests, semi-supervised learning incorporates components from both supervised and unsupervised ML by utilizing both the labeled and unlabeled datasets during the AI algorithm’s training. Deep learning (DL) is a subtype of ML that uses multi-layer artificial neural networks, which are often greater than 20 layers deep . A commoThe evaluation and comparison of AI algorithms is inherently challenging. This is not only difficult due to variation in AI architectures, but also due to variation in test datasets. Fortunately, there are several performance metrics that allow for the standardization and comparison of AI algorithms, several of which are presented in this paper. Some of the metrics commonly used when discussing diagnostic accuracy include true positives (TP), false positives (FP), true negatives (TN), false negatives (FN), sensitivity [TP/(TP+FN)], specificity [TN/(FP+TN)], accuracy [(TP+TN)/(TP+TN+FP+FN)], precision [TP/(TP+FP)], Dice/F1 score [2TP/(2TP+FP+FN)], and the area under the receiver operating characteristic (ROC) curve (AUC). For image data, the Dice score, equivalent to the F1 score, is a common metric for evaluating model segmentation performance by evaluating the spatial overlap (pixelwise agreement) between a gold-standard segmentation and model-derived segmentation, with scores ranging from 0 (no overlap) to 1 (exact overlap). It represents the harmonic mean of precision and sensitivity and therefore is commonly used to capture both values within one metric.Stroke is a leading cause of death and the number one cause of serious long-term disability in the United States, with ischemic stroke accounting for most stroke types . Over thAI advancements in ischemic stroke imaging have primarily focused on optimizing stroke workflows, the detection of large vessel occlusions (LVO), quantifying stroke scoring metrics, the segmentation and quantification of ischemic or at-risk tissue, and clinical stroke outcome prediction. This section will center on stroke segmentation, ischemic stroke detection, and the automation of stroke metrics. Current AI-based competitions and challenges related to stroke segmentation will also be discussed.ML and DL have already made a significant impact on current clinical stroke imaging practices. Currently, there are at least 14 different commercially available AI-based software packages related to ischemic stroke imaging ,12,13,14A major goal of stroke imaging is to evaluate and accurately segment ischemic penumbra volume from core infarct volume. MRI/DWI is the gold standard for evaluating infarct core volumes. However, it is typically slow, expensive, and not always readily available. Additionally, manual segmentation of ischemic changes is cumbersome, time-consuming, and affected by inter-rater variability . CT perfMany major AI commercial software packages provide threshold-based perfusion maps and segmentation analysis via DL models. Over the last several years, multiple new architectures and methods have been developed for stroke segmentation for both CT and MR, many of which have been developed and tested during recent AI competitions and challenges.https://www.isles-challenge.org/, accessed on 15 May 2023) was created in 2015 as an open competition to develop and encourage the design of advanced tools for use in ischemic stroke analysis. The most recently completed challenge in 2018 evaluated 24 teams’ ability to segment infarcted tissue from CTP images, with corresponding DWI images being the referenced standard [The Ischemic Stroke Lesion Segmentation (ISLES) challenge  is currently underway and has the goal of segmenting infarcted brain lesions using DWI, ADC, and FLAIR images. There have been over 90 entries from multiple countries, with the current highest DICE score of 0.78. An additional separate segmentation challenge using a larger standardized dataset of T1-weighted images known as ATLAS 2.0—Anatomical Tracing of Lesions After Stroke —is also underway [The 2022 ISLES challenge (underway . This chLarge-vessel occlusion (LVO) and a corresponding hyperdense artery sign are strong indicators of stroke on the CT angiogram (CTA) and non-contrast CT (NCCT), respectively. Many commercially available AI-based software packages provide LVO detection . These aThe Alberta Stroke Program Early CT Score (ASPECTS) is a quantitative 10-point scoring system used to assess patients who would benefit from endovascular therapy. The score is determined based on early ischemic changes visible on NCCT . The scaThe various available automated tasks described in this section have potential to facilitate the objective characterization of ischemic stroke to guide emergent treatment. While the overall performance of these methods is encouraging, the role of the experienced radiologist remains vital to the final interpretation and diagnosis.In the acute clinical setting, intracranial hemorrhage (ICH) represents a potentially life-threatening situation that demands prompt and accurate detection with imaging. Failure to detect and diagnose ICH in a timely manner can result in a delay of treatment and thus higher morbidity and mortality, underscoring the importance of early and accurate detection ,51,52. CDue to its high sensitivity and specificity, CT is the modality of choice for ICH detection. Many recent studies on ICH detection examined DL neural networks, and most studies evaluating specific algorithms were retrospective, often comparing the results to those obtained with experienced neuroradiologist(s). Overall, current software packages have performed well with ICH detection. For example, Heit et al. evaluated RAPID ICH detection of ICH compared to the results of 3 trained neuroradiologists, with sensitivities and specificities of 96 and 95%, respectively . This stNumerous software tools have been developed in the last decade, many with high-performance and expert-level accuracy in terms of the diagnosis of ICH. Implementing them into clinical practice could improve the quality of radiology workflow, effectively providing a “second” read or quality assurance to the reading radiologist. One major obstacle to robust performance, however, lies in single-center design and the small size of single-institution training and test datasets, limiting external validity and applicability to a larger-scale setting. One initiative to improve AI-based algorithms would be the creation of large-scale annotated datasets. AI challenge competitions provide this level playing field, allowing the nonbiased and validated performance comparison of algorithms’ performance.The first AI ICH detection challenge took place in 2019 when the Radiological Society of North America (RSNA) collaborated with the American Society of Neuroradiology (ASNR) . An 874,In addition to detection, hemorrhagic volume and expansion are important predictors of outcomes and treatment responses . Accuratp < 0.05). The top-performing models in their study had median Dice scores of 0.92 for ICH, 0.66 for perihematomal edema (PHE), and 1.00 for IVH. Notably, a nnU net algorithm, named Focal, achieved the highest Dice score with IVH. Not unexpectedly, a worse performance was noted for the detection of smaller hemorrhages.In recent years, researchers have investigated the ability of ML models to mitigate the aforementioned shortcomings of manual ICH segmentation. Patel et al. 2019 found that a convolutional neural network was able to match radiologists’ estimation of ICH volume . Islam ePrimary central nervous system (CNS) tumors are relatively rare neoplasms, with potentially devastating outcomes. Intracranial metastatic disease is the most common CNS tumor overall, accounting for more than half of all brain malignancies. Gliomas and meningiomas account for two thirds of all primary CNS tumors. High-grade gliomas (glioblastomas) are an often-catastrophic malignancy, with persistently low survival rates despite our improved understanding of the disease ,65. On tAdvancements in AI for brain tumors are broad and include research into tumor detection , segmentation (defining tumor boundaries), grading (determining aggressiveness of a tumor), prognostication , and treatment response assessment . Several research areas are related to an emerging field called radiogenomics (imaging genomics), which explores the correlations between imaging characteristics and genetic/mutational patterns, essentially providing a “virtual biopsy” based on imaging patterns and other clinical data . AlthougTumor detection and surveillance of disease is a common and important task for radiologists. Targeted therapies like stereotactic radiosurgery (SRS) have pushed radiologists to meticulously detect and track numerous CNS lesions, often adding significant time and effort for each study. In a time-constrained work environment, the potential to augment tumor detection via AI is appealing. Two recent meta-analyses by Ozkara et al. and Cho et al. demonstrated that classical ML and DL algorithms overall performed well in the detection of brain metastasis, with a pooled sensitivity of 89–90% ,71. As aThe segmentation of brain tumors involves accurately and reliably delineating brain tumors from normal tissue, other disease pathologies, and imaging artifacts. The segmentation of brain tumors is difficult due to the variable size and heterogenous appearance of CNS malignancies. Additionally, some tumors demonstrate poor contrast enhancement and can easily overlap in intensity with the surrounding healthy brain parenchyma. Tumor grading, disease prognosis, treatment options, and post-treatment monitoring all depend on accurate segmentation. To explore AI facilitation of these efforts, the Brain Tumor Segmentation (BraTS) challenges have been ongoing for 10 years, representing the crucial joint efforts of the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society to characterize gliomas and to improve algorithms designed for brain glioma segmentation, tumor compartmentalization, and tumor molecular characterization. New dataset analytics, including the most recent iterations of the BraTS challenge, have expanded case pathology and segmentation analytics beyond just gliomas to include meningiomas, pediatric brain, tumors, brain metastases, schwannomas, and other brain tumors .While there are several recognized brain tumor datasets, the BraTS datasets are some of the most widely used for training and testing AI-based algorithms that focus on tumor segmentation. These multi-institution datasets have been growing annually since 2012 and comprise hundreds of well-annotated images obtained using common MR sequences  of both high- and low-grade tumors. The datasets also consist of pooled ground truths determined by a set of expert raters who segment various components including edema, necrotic tumor core, and enhancing tumor. Numerous studies and papers related to tumor segmentation have been published in relation to the BraTS dataset over the years using various AI methods, including DL and other ML algorithms, with performance improving over the years ,77. InitRadiomics studies are using ML to assess brain tumor responses to clinical treatment, differentiate progression and pseudoprogression, and predict the recurrence and infiltration of neoplastic disease. It can often be hard to delineate tumor response and post-chemotherapy and radiation changes upon subsequent imaging. Further complicating the picture are new immunotherapies that incite complex inflammatory responses and antiangiogenic agents like bevacizumab that can cause a reduction in enhancement by reducing microvascular growth, but without necessarily offering any improvement in the overall survival rate .Pseudoprogression is characterized by increased extent of imaging abnormalities after treatment, including abnormal enhancement and T2/FLAIR signal changes, that typically resolve without intervention. Differentiating pseudoprogression from true tumor progression is vital to treatment success and clinical outcomes. AI methods and techniques, including radiomics, are currently being explored to differentiate true progression from pseudoprogression by incorporating features from advanced MRI techniques, including diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and MR spectroscopy . For exaDifferentiating infiltrating neoplasms from adjacent edema is difficult using conventional imaging approaches. However, ML has the capability to identify the margins of infiltrative tissue from normal tissue on pre-treatment MRI images. Identifying infiltrative margins is important in resection planning, biopsy site selection, and monitoring treatment response. Approaches that incorporate ML have been successful in generating spatial maps of infiltrated tissue, with approximately 90% cross-validated accuracy . A fullyWhite matter lesions (WMLs), or white matter hyperintensities (WMHs), are areas of abnormal myelination that can be best appreciated as increased signal intensity on T2/FLAIR sequences. They are widely utilized neuroradiologic biomarkers of brain parenchymal pathologies. As a group, they include but are not limited to small-vessel ischemic disease, demyelinating disease, or other inflammatory processes ,85. The https://grand-challenge.org/aiforradiology/, accessed on 15 May 2023).Due to the impact and location-dependent nature of these lesions, accurate detection and quantification is paramount. Traditional practices involve the manual delineation of regions via analysis dependent on visual, qualitative, or semiquantitative inspection of imaging, although even early studies recognized the benefit of automatic or semiautomatic image analysis. The utilization of neural networks and other AI approaches in quantifying white matter signal abnormalities and related findings such as brain volume changes has evolved over the past five years, with numerous methods already developed and commercially available ,94,95,96Some of the most promising clinical applications of these algorithms have included use in the automatic and semiautomatic segmentation of multiple-sclerosis (MS) lesions, age-related WMH, and in aspects of radiomics ,98. All Similar to early public databases such as BrainWeb computational phantom, competitions like the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) and WMH Segmentation Challenge serve as catalysts for the development of novel methods and provide multi-institutional comparisons of algorithms using standardized evaluation criteria ,104. SevThe 2017 MICCAI WMH Segmentation Challenge was held in Quebec, Canada, and originally included 20 participating teams. Teams were given a training dataset, containing 60 brain MR cases from three separate scanners, to develop their methods with and were then tested using a separate test dataset containing 110 MRI brain cases from five different scanners . Though Multiple sclerosis is a debilitating disease that often affects people in the prime of their lives. The prevalence of MS is growing, and the need to have automated accurate detection and quantification of lesion burden is crucial to disease management and prognosis .https://zenodo.org/record/5775523, accessed on 15 May 2023) [The first major MS lesion segmentation challenge occurred at the MICCAI 2008 conference. A second MS lesion segmentation challenge was conducted at the 2015 International Symposium on Biomedical Imaging (ISBI) in New York. The 2015 challenge and dataset are still being used and currently have over 2000 submissions. A subsequent 2016 segmentation challenge and dataset attempted to address multiple potential issues faced during the 2008 and 2015 challenges by using high-quality patient cases, drawn from four different scanners after delineation by seven expert evaluators, to reduce inter-rater variability. Evaluations utilized a distributed Web platform for the automatic, fair comparisons of algorithms. The challenge evaluated candidate algorithms’ performance for both detection  and segmentation (precisely outline lesions). Only 13 teams participated in the original challenge in 2016. In this event, F1 lesion detection scores ranged from 0.13 to 0.49 (avg 0.32), with an expert range of 0.66 to 0.89 (avg 0.77). Dice segmentation scores for the algorithms ranged from 0.27 to 0.59 (avg 0.46), with the expert Dice scores ranging from 0.69 to 0.78 (avg 0.71) ,113. It ay 2023) ,114.Dementia is an acquired syndrome characterized by a significant decline in cognitive function that leads to difficulty in daily functioning/independence, with an estimated prevalence in the United States of 11% in individuals over 65 years old . Mild coBecause early and accurate diagnosis of dementia and other neurocognitive disorders is imperative to allow access to supportive therapies that can help patients maintain their independence, ML methods based on neuroimaging have great potential to promote earlier and more sensitive diagnosis of neurocognitive disorders . For insNumerous challenges have been conducted to investigate the use of structural-MRI-based ML in the screening and diagnosis of dementia. While several challenges addressed research questions of aiding prediction of future outcomes and cognitive scores, the challenges that most pertain to the practicing neuroradiologist largely correspond to three clinical questions in dementia: screening, clinical status classification, and monitoring of disease progression ,123,124.Currently, approaches to screening for cognitive impairment include screening tests such as the mini-mental state examination (MMSE), clock drawing test, and Montreal cognitive assessment (MoCA), among others, as well as biological markers . Howeverr = 0.10 in order to reduce bias. Seventy-nine participating teams were provided with a large structural MRI training dataset of healthy individuals with ages provided (N = 2640) and tested on a test dataset obtained from the same institutions (N = 660). The winning team of Gong et al. used lightweight 3D convolutional neural networks (CNNs), combined with preprocessing steps and pre-trained on UK Biobank data (MAE = 2.95 years after bias correction) [The Predictive Analytics Competition (PAC) 2019 sought to improve ML models using whole-brain structural MRI scans from healthy individuals to predict brain age, a cumulative screening marker of functional capacity, residual lifespan, and the risk of progression to neurocognitive disease . Particirection) .PAC 2019 revealed that current ML models are increasingly effective at predicting brain age without incurring significant bias. DL models also outperformed classic ML algorithms . CNNs usWhile this challenge demonstrated the efficacy of ML models in the specific task of brain age prediction, the cost and availability of structural MRI acquisition for the general population of healthy adults as a screening test for cognitive impairment remains a barrier to the application of these models in clinical practice. Brain age prediction through these ML models may be more useful for populations that are predisposed to cognitive impairment, such as those with a family history or other known biomarkers.In current clinical practice, the initial evaluation of cognitive impairment includes elements of clinical history, neurologic examinations with an emphasis on mental status, labs to screen for reversible causes of cognitive dysfunction , and structural brain imaging, with MRI being the preferred method over CT . ClassifN = 30) of patients that were equally representative of the 3 diagnostic classes. Participants were also allowed to use extra training data, and most used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and/or the Australian Imaging Biomarker and Lifestyle flagship study of aging (AIBL). Algorithms were tested against a previously unseen multi-center CADDementia dataset (N = 354) with clinical diagnosis established via a multi-disciplinary consensus that was blinded to the participants. The performance of the algorithms was quantified by classification accuracy, area under the receiver operating characteristic (ROC) curve (AUC), and the true positive fraction for the three classes.The CADDementia challenge was conducted in 2014 and gave participants the task of classifying baseline MRI scans into three diagnostic classes: Alzheimer’s disease, mild cognitive impairment (MCI) and cognitively normal (CN) . FifteenN = 19 algorithms), cortical thickness, intensity, and shape. Algorithms also utilized a variety of classifiers, such as SVM classifier, random forest classifier, and linear discriminant analysis (LDA). Only one algorithm (Folego-ADNet) used a convolutional neural network for classification and achieved a relatively low rank [The challenge submissions utilized a wide range of approaches, with most methods accounting for input features such as volume  challenge was conducted to develop and compare methods of estimating atrophy and rates of atrophy from structural MRI . In partThe MIRIAD dataset consisted of 708 T1-weighted volumetric scans acquired from 69 subjects , with each subject undergoing 1–12 scans over 1–2 years at various time intervals. Submissions were graded based on the predicted sample size requirements for a hypothetical clinical trial, assuming a putative treatment effect of a 25% atrophy rate reduction . The ratChallenge participants produced consistent and repeatable measures of change in brain regions and ventricles; however, hippocampal measures were the most variable among the submissions. Cash et al. attribute this variance to the differing definitions of the hippocampus used in segmentation protocols, as well as the possibility that the hippocampus is a small structure of the brain susceptible to MRI acquisition artifacts . The besThe application of the results of the MIRIAD challenge to clinical practice would be limited to the occasional cases of cognitive impairment in which serial neuroimaging is acquired. Several studies have attempted to predict the conversion of MCI into AD using baseline data, with accuracies as high as 83% when incorporating the results of multiple modalities, including structural MRI, PET, CSF, and clinical metrics . This prBack pain is a common problem and one of the leading causes of disability in both developed and developing countries ,137. DegAccurate spine labeling is a critically important aspect of imaging interpretation as it establishes the numerical and categorical relationship of one vertebra to another. Manual labeling can be time-consuming and error-prone, particularly when variant anatomy exists. In the setting of AI, several definitions pertain to the process of assigning a specific identifier to a vertebral body. Localization is the detection of a distinct three-dimensional unit in space , and labeling assigns an identifier or class to the three-dimensional unit. Segmentation can be thought of as a voxel-level labeling task . AutomatSegmentation is a demanding task, both for radiologists and algorithms. While manual segmentation is possible, it is not practical in the clinical setting. For example, it is very time-consuming to segment the posterior elements manually. AI-based segmentation algorithms may suffer from an insufficient volume of adequate training datasets. In conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), the Large Scale Vertebrae Segmentation Challenge (VerSe) was organized in 2019 and 2020 to tackle this ongoing problem. A large training dataset was created that included 374 spine CT exams annotated through semi-automated techniques, with additional manual refinement . A totalIntervertebral discs (IVD) have also been targets for automated localization and segmentation in efforts to help identify and quantify degenerative disc disease with the goals of minimizing error and time spent on manual interpretation ,142. Li There are numerous applications once accurate segmentation is achieved, including the evaluation of degenerative changes. In 2017, Jamaluden et al. developed a localization algorithm using T2-weighted sagittal spine MRI images with an accuracy of 95.6% . Their aVertebral fractures in the thoracic and lumbar spine have also been targets of AI automation and assistance. Compression fractures may be overlooked by radiologists, especially those who do not frequently read spine imaging . Burns eThe evaluation of scoliosis is another prime target for AI, including Cobb angle measurements. Issues of reproducibility, accuracy, and the inter-rater reliability of clinicians manually measuring these angles could potentially be addressed using AI. Wang et al. proposed a multi-view extrapolation net (MVE-Net) to estimate Cobb angles using AP and lateral radiographs . The autArtificial intelligence (AI) has the potential to revolutionize head and neck imaging by augmenting image quality and improving its ability to perform clinically relevant tasks such as tumor volume segmentation, tumor characterization, tumor prognostication, treatment response assessment, and the prediction of metastatic lymph node disease ,155. Heahttps://han-seg2023.grand-challenge.org/, accessed on 15 May 2023) was launched recently to promote the development of new and existing applications of fully automated techniques for OAR (organ-at-risk) segmentation in the head and neck regions of CT images. The goal of this challenge is to exploit the information of multiple imaging modalities in order to improve the accuracy of segmentation results. The HEad and neCK TumOR (HECKTOR) challenges  focused on establishing best-performing methods in order to predict patient outcomes from FDG-PET/CT and clinical data and conduct the automatic segmentation of head and neck primary tumors and lymph nodes on FDG-PET/CT images.AI has also been applied in the prognostication of responses to chemotherapy or radiation in head and neck cancer . In addiAI has the potential to significantly improve the accuracy and efficiency of ultrasound use in head and neck oncology. A systematic review by Santer et al. found that 74% of studies on the use of AI in ultrasound for head and neck oncology addressed disease diagnosis, with 56% examining the ability to distinguish benign and malignant thyroid nodules and 44% seeking to identify metastatic lymph nodes . RadiomiThe increasing number of independent prognostic and predictive markers has sparked interest in the use of AI-based prediction models. AI-based methods can integrate complex imaging, histologic, molecular, and clinical data to model tumor biology and behavior, and potentially identify associations far beyond what conventional qualitative imaging can provide alone. DL-based models can more accurately predict oncological outcomes using pre-treatment data than existing models. Likewise, they are better at predicting treatment toxicity prior to the start of treatment as well as the prediction of pathological data from imaging data .CTA is a widely used and cost-effective imaging modality for the diagnosis of cerebrovascular disease in the head and neck region. However, manual postprocessing of CTA images can be time-consuming and subject to human error. DL-based segmentation approaches have been proposed as having the potential to improve the efficiency of CTA analysis by reducing the need for manual post-processing. One major challenge in CTA image postprocessing is the accurate segmentation of vessels, exacerbated by their branching morphology, variable anatomy, and overlap in density with other tissues. To address these challenges, Fu et al. developed an automatic imaging reconstruction system called CerebralDoc, which uses a 3D-CNN containing modified U-net components for the reconstruction of original head and neck CTA images . This syTraditionally, the noninvasive diagnosis of cerebral aneurysms has relied on imaging modalities such as CTA or magnetic resonance angiography (MRA). However, these techniques can be limited in their ability to accurately detect and classify small or complex aneurysms. AI has the potential to improve the accuracy of cerebral aneurysm diagnosis by automating the analysis of imaging data and identifying subtle features that may be overlooked by human observers. In a study by Park et al., a DL-based model called HeadXNet was developed for the diagnosis of cerebral aneurysms using CTA images . The modDL-based models have also been developed in order to improve the accuracy of cerebral aneurysm rupture risk prediction. In a study by Yang et al., a CNN-based DL model was developed for the prediction of cerebral aneurysm rupture risk using 3D time-of-flight magnetic resonance angiography (TOF-MRA) images . The modArtificial intelligence has numerous applications throughout the field of neuroradiology, with great promise to augment the work of the modern-day radiologist. AI-based methods have taken large strides in accuracy and efficiency over the past decade, some of which can be linked to the AI challenge competitions conducted to benchmark leading AI methodologies using well-annotated datasets. As demonstrated in this article, AI applications have been developed and evaluated for use in detecting or quantifying intracranial hemorrhage and stroke, brain and head/neck tumors, spinal fractures, degenerative spinal disease, and inflammatory or neurodegenerative brain disorders. While many AI methods demonstrate remarkable performance in specific tasks and several software packages have been approved for clinical use, there remains a continuous need to push the field further to develop improved tools with which to better augment the work of the practicing neuroradiologist. AI-based challenges will likely continue to showcase the latest advancements in AI methods and provide an impetus for improvement, ultimately leading to higher-quality patient care.


Indigenous and American Indian Alaskan Native (AI/AN) community members are systematically underrepresented in clinical trial research. This paper focuses on exploratory steps to partner with Native Nations of Arizona to engage Community Health Representatives (CHR) as a trusted source for building COVID-19 clinical trial research, including vaccine trials awareness. CHRs are frontline public health workers who apply a unique understanding of the experience, language, and culture of the population served. This workforce has entered the spotlight as essential to the prevention and control of COVID-19. Methods: Three Tribal CHR programs were engaged to develop and refine culturally centered educational materials and a pre-post survey using a consensus-based decision-making approach. CHRs used these materials in brief education sessions during regular client home visits and community events. Results: At 30 days post CHR intervention, participants (N = 165) demonstrated significantly increased awareness about and ability to enroll in COVID-19 treatment and vaccine trials. Participants also described a significant increase in trust in researchers, decreased perceived barriers related to cost for participation in a clinical trial, and improved belief that participation in a COVID-19 clinical trial for treatment was considered a benefit to American Indian and Alaskan Native people. Conclusion: CHRs as trusted sources of information, coupled with culturally centered education materials designed by CHRs for CHR clients, demonstrated a promising approach to improved awareness of clinical trial research generally and COVID-19 trials specifically among Indigenous and American Indian community members of Arizona. Historical and contemporary inclusion of racial and ethnic diversity is paramount to eliminating health disparities and achieving health equity. Indigenous and American Indian and Alaskan Native (AI/AN) people are systematically underrepresented in clinical trial research ,2. In thSeveral social and structural barriers exist for Indigenous and AI/AN representation within clinical trial research, including western scientific constructs that are most often conducted by institutions from dominant non-Indigenous cultures . These iConducting culturally centered clinical trial research in collaboration with AI/AN communities has been evidenced as the primary facilitator for AI/AN engagement in trials . CulturaBy the beginning of September of 2020, the National Institutes of Minority Health and Health Disparities (NIMHD) had launched several priority initiatives to promote community-engaged research to respond to the COVID-19 pandemic in the United States. This paper describes the results of a partnership between two of these NIMHD-funded COVID-19 response initiatives. Specifically, this paper focuses on exploratory steps to leverage these initiatives to engage CHRs as a trusted source for building awareness and trust in information about COVID-19 clinical trial research, including vaccine trials. In Arizona, the CHR workforce has entered the spotlight as essential to COVID-19 prevention and control . We specThe NIMHD Community Engagement Alliance (CEAL) Against COVID-19 Disparities initiative offered funding to 11 U.S. states, including Arizona, to conduct outreach and engagement efforts in ethnic and racial minoritized communities disproportionately affected by the COVID-19 pandemic . In partThe Southwest Health Equity Research Collaborative (SHERC) is a NIMHD grant-funded cooperative agreement operated by the Center for Health Equity Research at Northern Arizona University. The goal of the SHERC is to increase basic biomedical, clinical, and behavioral research at NAU to address health disparities and advance health equity among diverse populations of the southwestern United States . In SeptThe CEAL project focused on engaging the broader Community Health Worker workforce in Arizona, inclusive of the tribally employed CHR workforce, to build workforce capacity to address COVID-19 prevention and vaccine uptake. CEAL researchers developed a CHR survey and focus group interview guide to engage CHRs (N = 45) in understanding topics important to the development of COVID-19 education materials. ,14 TopicAlthough not the focus of this paper, CEAL focus group data were audio recorded and transcribed verbatim by Indigenous and non-Indigenous research staff. Using a code book, research staff independently coded focus group transcripts and, through a process of consensus, identified common themes for each focus group discussion. Before participating in the focus groups, CHRs also completed a brief survey that collected demographic information, vaccine status, and COVID-19 prevention behaviors. Results from the brief survey and focus groups with CHRs about clients’ experiences, beliefs, and behaviors related to the prevention of COVID-19 were shared back with the project team and used to inform the development of the educational materials for this project .Three CHR Programs with long-standing relationships with the NAU Center for Health Equity Research were invited to participate in the CEAL and SHERC funded projects, including the Salt River Pima-Maricopa Indian Community, White Mountain Apache Tribe, and Colorado River Indian Tribes. A total of 15 CHRs participated.t-test for significance was performed to assess mean change from pre- to post-survey. For the purposes of this paper, we focus on attitudes and planned behavior related to awareness and participation among AI community members in clinical research.Building from the knowledge gained through the CEAL-supported CHR focus groups, the collaborative research teams used a consensus-based decision-making methodology to review and modify vaccine education materials with CHRs, inclusive of education about the purpose of clinical research trials. Consensus-based decision-making, or a consensus panel, involves a facilitative group decision-making process . In sum,Of the 15 CHRs in the three CHR programs, a total of 11 CHRs participated in the CEAL focus groups in 2021. During this formative step, CHRs revealed the context necessary for understanding how to develop culturally meaningful and safe communication and educational materials about COVID-19, including vaccines and participation in clinical trial research. CHRs expressed several challenges faced by their AI clients as a result of the pandemic and described the numerous ways in which everyday life was deeply affected. Specifically, CHRs described clients being impacted at the individual, family, and community levels and by the physical and social isolation experienced during COVID-19. CHRs believed these processes led to mental health issues such as depression and anxiety as well as difficulty managing existing chronic health conditions while isolated alone at home.During this time, CHRs also described how they directly provided support to their clients and community throughout the pandemic. Specifically, CHRs addressed the following primary fears among their AI clients: vaccine safety and potential health risks, vaccine side effects, vaccine long-term health effects, and mistrust in the federal government. CHRs raised five primary recommendations to prevent COVID-19 and improve vaccine confidence among AIs: developing culturally and linguistically relevant policies, programs, and resources to support elderly, homebound, disabled and non-English speaking communities; generate culturally and linguistically relevant mental health, isolation, grief, and loss resources; integrate COVID-19 prevention with chronic disease management, especially during periods of isolation and quarantine; improve public trust to dispel myths and correct misinformation and enhance update of public health recommendations; and invest in the professional development of the CHR workforce to promote culturally and linguistically relevant evidence-based materials and tools.Data gathered through this first formative step was critical for the broader research teams to understand the context of our partners. SHERC research staff were invited to review and dialogue about CEAL findings which informed the development of the educational materials prior to the consensus panels. Our collective CEAL and SHERC teams also drew upon recommendations provided by CHRs involved in the CEAL focus groups regarding the ways they know clients gather, process, and act on information. CHR recommendations for designing culturally appropriate messaging and education about COVID-19 and beyond are presented in As a result of CHR-led brief intervention with culturally adapted educational materials explained in detail elsewhere  awarenesAt baseline, at least half of respondents were not willing nor likely to enroll in a COVID-19 clinical trial; only about 10% said they were willing or likely to enroll. Despite the reported low levels of interest in participating in clinical trials, participants reported recognizing the value of potential participation. At follow-up, almost two-thirds of participants reported their involvement in a COVID-19 clinical trial would make them feel like they were helping their community and helping discover treatments. In addition, participants recognized that participating in clinical trials would benefit AI/AN people outside their own communities to get COVID-19 treatment. Additionally, almost two-thirds of participants reported at follow-up that participating in a COVID-19 clinical trial for treatment would help people like them get treatment for COVID-19.At follow-up, the top three reasons why participants would not participate in a COVID-19 clinical trial were related to not understanding what would happen to them (36%), not trusting the government (36%) or a general concern that the COVID-19 vaccine may not be safe (17%). Trusting in researchers significantly improved from pre to post, while awareness that participation in a clinical trial would not cost money significantly decreased from pre to post. Concern about a chronic condition that might prevent them from participating in research increased slightly from pre to post.Alternatively, and although less reported as a barrier at baseline, issues related to time, transportation, importance of research and not believing vaccines are safe all de-creased from baseline to post-survey.Here, we describe a robust partnership between two national NIMHD-funded initiatives aimed at rapidly engaging community partnerships to address COVID-19 health inequities: CEAL and SHERC. Specifically, we describe how, through our partnership, researchers engaged the tribally employed CHR workforce of Arizona to learn best practices to engage AI community members in COVID-19 prevention and vaccine and clinical trial participation awareness. Through the Arizona CEAL project, CHRs were engaged in formative focus groups and taught researchers the community context and cultural and traditional ways of engaging Indigenous citizens in discussing COVID-19 prevention and vaccine uptake. The SHERC supplement project leveraged this local knowledge to directly collaborate with three Arizona CHR Programs to rapidly adapt and implement culturally and contextually salient educational materials to support CHRs as a trusted source to deliver brief education. Results demonstrate that although willingness to participate in a COVID-19 clinical trial for vaccines and/or treatment were mixed, there was visible progress toward trust and participation, including significant improvement in trust in researchers, awareness of existing trials and how to sign up improved through the CHR-led brief intervention. We also observed improvement in participant attitudes about the potential benefit of clinical trials, while barriers of cost of participation in research decreased.CEAL formative focus groups also documented the multitude of ways CHRs provide accurate information to address COVID-19 and vaccine misinformation to increase the likelihood of their clients making an informed decision. CHRs described understanding that their clients and community members were afraid and lacked trust in COVID-19 information, particularly in the federal government and the safety of the vaccine. This fear and distrust may in turn dissuade participation in research generally, and especially research involving the COVID-19 vaccine or treatment. CHRs also described how, when focused on prevention, they attempted to generate confidence in COVID-19 facts. In this way, CHRs believed they reduced vaccine hesitancy and reported clients expressing a desire to get vaccinated after their concerns and questions were addressed. According to CHR participants in the CEAL focus groups, CHRs are considered trusted and influential role models within tribal communities and to their clients. CHRs believed people trust the CHR Program to provide accurate and updated information, look upon them for guidance, and follow their lead. CHRs were aware of their role model status and, therefore, dedicated time to staying informed and updated on all things related to COVID-19. In this way, they were prepared to answer client’s questions with confidence, refer them to reputable sources of information, and help them make informed decisions. Particularly for the vaccine, CHRs reported using their own experience with the vaccine to communicate with clients who may be vaccine hesitant. CHRs prioritized support of people’s autonomy and respect for their opinions and choices. Information shared by Arizona CHRs is consistent with broader United States CHW workforce involved in COVID-19 prevention and vaccine uptake efforts .Our results suggest great potential of the CHR workforce and programs in addressing the social and structural determinants to research participation among underrepresented populations. Despite participants reporting less willingness to participate in clinical trials in the future, use of educational materials developed in collaboration with Indigenous and non-Indigenous researchers through consensus panels was observed to raise awareness about COVID-19-related vaccine and treatment trials available in the state among participants from pre- to post-intervention. Moreover, CHRs, through their trusted relationships and culturally relevant educational materials, were also able to improve participants knowledge of how to sign up for a clinical trial. Each of these engagement methods is in accordance with emerging research on this topic .Through our research, we also learned the primary reason for not participating in a COVID-19 vaccine or treatment research among participants. These barriers included not understanding what would happen during the research, and lack of trust in government, the vaccine and/or researchers. These issues may be rooted in the social and structural issues attached to western research approaches that have historically marginalized the participation of diverse populations. Conducting culturally centered research with and for AIs represents a shift away from western research approaches and toward an Indigenous research paradigm rooted in respect and reciprocity .Through culturally centered, community-based research approaches, Indigenous and non-Indigenous researchers can build stronger bridges with Native Nations to boost AI/AN representation across the United States and Canada in research studies, especially clinical trial research . EmerginAt the federal level, the NIH COVID-19 Prevention Trials Network (CoVPN)  launchedIn sum, the strengths of this study include a diverse representation of Indigenous leadership within both the research and community partner teams, and long-standing trusted partnership between academic institutions and CHR Programs. Our limitations are important to mention as our data are not generalizable, any associations should be made with caution due to the exploratory nature of this study.Full representation of diverse communities in clinical trial research is fundamental to advancing health equity. Our study demonstrates how engagement of the CHR workforce, who are known widely as the ‘pulse of the people’, may serve as agents of change. CHRs can inform efforts to effectively engage diverse and underrepresented populations in research and improve western academic institutions’ approach to ensure full representation of all people in research.