A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS: We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS: A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS: Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.

Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance. Hepatic steatosis and fibrosis were identified by vibration-controlled transient elastography. Receiver operating curve was used to assess the relationship of anthropometric indicators, e.g., the FMI, body mass index (BMI), weight-adjusted-waist index (WWI), percentage of body fat (BF%), waist-to-hip ratio (WHR), and appendicular skeletal muscle index (ASMI), with hepatic steatosis and fibrosis. In this study, which included 2260 participants, multivariate logistic regression models, stratified analyses, restricted cubic spline (RCS), and sharp regression discontinuity analyses were utilized. The results indicated that the WHR and the FMI achieved the highest area under the curve for identifying hepatic steatosis and fibrosis, respectively (0.720 and 0.726). Notably, the FMI presented the highest adjusted odds ratio for both hepatic steatosis (6.40 [4.91-8.38], p = 2.34e-42) and fibrosis (6.06 [5.00, 7.37], p = 5.88e-74). Additionally, potential interaction effects were observed between the FMI and variables such as the family income-to-poverty ratio, smoking status, and hypertension, all of which correlated with the presence of liver fibrosis (p for interaction < 0.05). The RCS models further confirmed a significant positive correlation of the FMI with the controlled attenuation parameter and liver stiffness measurements. Overall, the findings underscore the strong link between the FMI and liver conditions, proposing the FMI as a potential straightforward marker for identifying liver diseases.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

BACKGROUND: Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. METHODS: Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48 h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. RESULTS: A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. CONCLUSIONS: Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework.

Background and aim: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements. Methods: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets. Results: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61). Conclusions: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Day-night pattern of acute ST-segment elevation myocardial infarction onset in patients with obstructive sleep apnea.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is associated with acute nocturnal hemodynamic and neurohormonal abnormalities that may increase the risk of coronary events, especially during the nighttime. This study sought to investigate the day-night pattern of acute ST-segment elevation myocardial infarction (STEMI) onset in patients with OSA and its impact on cardiovascular adverse events. METHODS: We prospectively enrolled 397 patients with STEMI, for which the time of onset of chest pain was clearly identified. All participants were categorized into non-OSA (n = 280) and OSA (n = 117) groups. The association between STEMI onset time and major adverse cardiovascular and cerebrovascular events was estimated by Cox proportional hazards regression. RESULTS: STEMI onset occurred from midnight to 5:59 am in 33% of patients with OSA, as compared with 15% in non-OSA patients (P < .01). For individuals with OSA, the relative risk of STEMI from midnight to 5:59 am was 2.717 [95% confidence interval (CI) 1.616 - 4.568] compared with non-OSA patients. After a median of 2.89 ± 0.78 years follow-up, symptom onset time was found to be significantly associated with risk of major adverse cardiovascular and cerebrovascular events in patients with OSA, while there was no significant association observed in non-OSA patients. Compared with STEMI presenting during noon to 5:59 pm, the hazard ratios for major adverse cardiovascular and cerebrovascular events in patients with OSA were 4.683 (95% CI 2.024 - 21.409, P = .027) for midnight to 5:59 am and 6.964 (95% CI 1.379 - 35.169, P = .019) for 6 pm to midnight, whereas the hazard ratios for non-OSA patients were 1.053 (95% CI 0.394 - 2.813, P = .917) for midnight to 5:59 am and 0.745 (95% CI 0.278 - 1.995, P = .558) for 6 pm to midnight. CONCLUSIONS: Patients with OSA exhibited a peak incidence of STEMI between midnight and 5:59 am, which showed an independent association with cardiovascular adverse events. CITATION: Wang Y, Buayiximu K, Zhu T, et al. Day-night pattern of acute ST-segment elevation myocardial infarction onset in patients with obstructive sleep apnea. J Clin Sleep Med. 2024;20(5):765-775.

Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms.

Background: Neuroendocrine neoplasms (NENs) are tumors that originate from secretory cells of the diffuse endocrine system and typically produce bioactive amines or peptide hormones. This paper describes the development and validation of a predictive model of the risk of lymph node metastasis among gastric NEN patients based on machine learning platform. Methods: In this investigation, data from 1256 patients were used, of whom 119 patients from the First Affiliated Hospital of Soochow University in China and 1137 cases from the surveillance epidemiology and end results (SEER) database were combined. Six machine learning algorithms, including the logistic regression model (LR), random forest (RF), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), were used to build the predictive model. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Results: Among the 1256 patients with gastric NENs, 276 patients (21.97 %) developed lymph node metastasis. T stage, tumor size, degree of differentiation, and sex were predictive factors of lymph node metastasis. The RF model achieved the best predictive performance among the six machine learning models, with an AUC, accuracy, sensitivity, and specificity of 0.81, 0.78, 0.76, and 0.82, respectively. Conclusion: The RF model provided the best prediction and can help physicians determine the lymph node metastasis risk of gastric NEN patients to formulate individualized medical strategies.

Towards Personalized Medicine for Chronic Liver Disease.

Chronic liver disease is a progressive deterioration of hepatic functions and a continuous process of inflammation, destruction, and regeneration of liver parenchyma, resulting in fibrosis and cirrhosis [...].

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

Development of a scoring system for predicting primary resistance to venetoclax plus hypomethylating agents (HMAs) in acute myeloid leukemia patients.

In recent years, one of the most promising advances in the treatment of acute myeloid leukemia (AML) is the combination of a hypomethylating agent (HMA) with the BCL2 inhibitor venetoclax (VEN). To better understand the key factors associated with the response of VEN plus HMA, 212 consecutive AML patients were retrospectively recruited to establish and validate a scoring system for predicting the primary resistance to VEN-based induced therapy. All AML patients were divided randomly into a training set (n = 155) and a validation set (n = 57). Factors were selected using a multivariate logistic regression model, including FAB-M5, myelodysplastic syndrome-secondary acute myeloid leukemia (MDS-sAML), RUNX1-RUNX1T1 and FLT3-ITD mutation (FLT3-ITDm). A nomogram was then constructed including all these four predictors. The nomogram both presented a good performance of discrimination and calibration, with a C-index of 0.770 and 0.733 in the training and validation set. Decision curve analysis also indicated that the nomogram was feasible to make beneficial decisions. Eventually a total scoring system of 8 points was developed, which was divided into three risk groups: low-risk (score 0-2), medium-risk (score 3-4), and high-risk (score 5-8). There was a significant difference in the nonremission (NR) rate of these three risk groups (22.8% vs. 60.0% vs. 77.8%, p < 0.001). After adjustment of the other variables, patients in medium- or high-risk groups also presented a worse event-free survival (EFS) than that in the low-risk group (hazard ratio [HR] = 1.62, p = 0.03). In conclusion, we highlighted the response determinants of AML patients receiving a combination therapy of VEN plus HMAs. The scoring system can be used to predict the resistance of VEN, providing better guidance for clinical treatment.

Enhanced tyrosine sulfation is associated with chronic kidney disease-related atherosclerosis.

BACKGROUND: Chronic kidney disease (CKD) accelerates atherosclerosis, but the mechanisms remain unclear. Tyrosine sulfation has been recognized as a key post-translational modification (PTM) in regulation of various cellular processes, and the sulfated adhesion molecules and chemokine receptors have been shown to participate in the pathogenesis of atherosclerosis via enhancement of monocyte/macrophage function. The levels of inorganic sulfate, the essential substrate for the sulfation reaction, are dramatically increased in patients with CKD, which indicates a change of sulfation status in CKD patients. Thus, in the present study, we detected the sulfation status in CKD patients and probed into the impact of sulfation on CKD-related atherosclerosis by targeting tyrosine sulfation function. RESULTS: PBMCs from individuals with CKD showed higher amounts of total sulfotyrosine and tyrosylprotein sulfotransferase (TPST) type 1 and 2 protein levels. The plasma level of O-sulfotyrosine, the metabolic end product of tyrosine sulfation, increased significantly in CKD patients. Statistically, O-sulfotyrosine and the coronary atherosclerosis severity SYNTAX score positively correlated. Mechanically, more sulfate-positive nucleated cells in peripheral blood and more abundant infiltration of sulfated macrophages in deteriorated vascular plaques in CKD ApoE null mice were noted. Knockout of TPST1 and TPST2 decreased atherosclerosis and peritoneal macrophage adherence and migration in CKD condition. The sulfation of the chemokine receptors, CCR2 and CCR5, was increased in PBMCs from CKD patients. CONCLUSIONS: CKD is associated with increased sulfation status. Increased sulfation contributes to monocyte/macrophage activation and might be involved in CKD-related atherosclerosis. Inhibition of sulfation may suppress CKD-related atherosclerosis and is worthy of further study.

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning.

BACKGROUND: Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. METHODS: EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. RESULTS: Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. CONCLUSION: The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.

[Application of machine learning model based on XGBoost algorithm in early prediction of patients with acute severe pancreatitis].

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS: A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.

Predicting the Recurrence of Common Bile Duct Stones After ERCP Treatment with Automated Machine Learning Algorithms.

BACKGROUND: Recurrence of common bile duct stones (CBDs) commonly happens after endoscopic retrograde cholangiopancreatography (ERCP). The clinical prediction models for the recurrence of CBDs after ERCP are lacking. AIMS: We aim to develop high-performance prediction models for the recurrence of CBDS after ERCP treatment using automated machine learning (AutoML) and to assess the AutoML models versus the traditional regression models. METHODS: 473 patients with CBDs undergoing ERCP were recruited in the single-center retrospective cohort study. Samples were divided into Training Set (65%) and Validation Set (35%) randomly. Three modeling approaches, including fully automated machine learning (Fully automated), semi-automated machine learning (Semi-automated), and traditional regression were applied to fit prediction models. Models' discrimination, calibration, and clinical benefits were examined. The Shapley additive explanations (SHAP), partial dependence plot (PDP), and SHAP local explanation (SHAPLE) were proposed for the interpretation of the best model. RESULTS: The area under roc curve (AUROC) of semi-automated gradient boost machine (GBM) model was 0.749 in Validation Set, better than the other fully/semi-automated models and the traditional regression models (highest AUROC = 0.736). The calibration and clinical application of AutoML models were adequate. Through the SHAP-PDP-SHAPLE pipeline, the roles of key variables of the semi-automated GBM model were visualized. Lastly, the best model was deployed online for clinical practitioners. CONCLUSION: The GBM model based on semi-AutoML is an optimal model to predict the recurrence of CBDs after ERCP treatment. In comparison with traditional regressions, AutoML algorithms present significant strengths in modeling, which show promise in future clinical practices.

Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review.

BACKGROUND AND AIMS: Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS: Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS: A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS: DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.

Anti-SARS-CoV-2 spike immunoglobulin G and immunoglobulin M titers decline as interval from the second inactivated vaccine dose to the onset of illness is prolonged in breakthrough infection patients.

BACKGROUND: Understanding of the early immune response in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) breakthrough infections is limited. METHODS: Ninety-eight patients with coronavirus disease 2019 (COVID-19) breakthrough infections were divided into two groups, with intervals from receiving the second dose of inactivated vaccine to the onset of illness <60 or ≥60 days. RESULTS: The median lymphocyte count and the median anti-SARS-CoV-2 spike immunoglobulin G (IgG) and immunoglobulin M (IgM) titers were higher in the <60-day interval group compared with the corresponding medians in the ≥60-day interval group (p = 0.005, p = 0.001, and p = 0.001, respectively). The median interleukin-6 (IL-6) level in the <60-day interval group was significantly lower than the median IL-6 level in the ≥60-day interval group (p < 0.001). CONCLUSIONS: Our results highlight the different anti-SARS-CoV-2 spike IgG and IgM antibody titers among patients with different intervals from receiving the second dose of inactivated vaccine to the onset of illness.

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification.

This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew's correlation coefficient (0.936 and 0.884), and Cohen's kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.