A commensal protozoan attenuates Clostridioides difficile pathogenesis in mice via arginine-ornithine metabolism and host intestinal immune response.

Antibiotic-induced dysbiosis is a major risk factor for Clostridioides difficile infection (CDI), and fecal microbiota transplantation (FMT) is recommended for treating CDI. However, the underlying mechanisms remain unclear. Here, we show that Tritrichomonas musculis (T.mu), an integral member of the mouse gut commensal microbiota, reduces CDI-induced intestinal damage by inhibiting neutrophil recruitment and IL-1ß secretion, while promoting Th1 cell differentiation and IFN-γ secretion, which in turn enhances goblet cell production and mucin secretion to protect the intestinal mucosa. T.mu can actively metabolize arginine, not only influencing the host's arginine-ornithine metabolic pathway, but also shaping the metabolic environment for the microbial community in the host's intestinal lumen. This leads to a relatively low ornithine state in the intestinal lumen in C. difficile-infected mice. These changes modulate C. difficile's virulence and the host intestinal immune response, and thus collectively alleviating CDI. These findings strongly suggest interactions between an intestinal commensal eukaryote, a pathogenic bacterium, and the host immune system via inter-related arginine-ornithine metabolism in the regulation of pathogenesis and provide further insights for treating CDI.

Multi-step validation of a Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis prediction system integrating multi-modal data: A multi-center study.

BACKGROUND AND STUDY AIMS: The impact of various categories of information on the prediction of Post Endoscopic Retrograde Cholangiopancreatography Pancreatitis (PEP) remains uncertain. We aimed to comprehensively investigate the risk factors associated with PEP by constructing and validating a model incorporating multi-modal data through multiple steps. PATIENTS AND METHODS: A total of 1,916 cases underwent ERCP were retrospectively collected from multiple centers for model construction. Through literature research, 49 electronic health record (EHR) features and one image feature related to PEP were identified. The EHR features were categorized into baseline, diagnosis, technique, and prevent strategies, covering pre-ERCP, intra-ERCP, and peri-ERCP phases. We first incrementally constructed models 1-4 incorporating these four feature categories, then added the image feature into models 1-4 and developed models 5-8. All models underwent testing and comparison using both internal and external test sets. Once the optimal model was selected, we conducted comparison among multiple machine learning algorithms. RESULTS: Compared with model 2 incorporating baseline and diagnosis features, adding technique and prevent strategies (model 4) greatly improved the sensitivity (63.89% vs 83.33%, p<0.05) and specificity (75.00% vs 85.92%, p<0.001). Similar tendency was observed in internal and external tests. In model 4, the top three features ranked by weight were previous pancreatitis, NSAIDS, and difficult cannulation. The image-based feature has the highest weight in model 5-8. Lastly, model 8 employed Random Forest algorithm showed the best performance. CONCLUSIONS: We firstly developed a multi-modal prediction model for identifying PEP with clinical-acceptable performance. The image and technique features are crucial for PEP prediction.

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. METHODS: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi-supervised algorithms. Then we selected diagnosis-related features through literature research and developed feature-extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature-extraction models and sole DL model were combined and inputted into seven machine-learning (ML) based fitting-diagnosis models. The optimal model was selected as ENDOANGEL-WD (whitish-diagnosis) and compared with endoscopists. RESULTS: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL-WD. ENDOANGEL-WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). CONCLUSIONS: We developed a novel system ENDOANGEL-WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.

BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS: A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS: For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS: We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy.

BACKGROUND AND AIMS: The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification. METHODS: We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences. RESULTS: The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices. CONCLUSIONS: We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

INTRODUCTION: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging. OBJECTIVES: This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies. METHODS: We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results. RESULTS: Experimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo. CONCLUSION: These data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Conditional Knockout of IL-1R1 in Endothelial Cells Attenuates Seizures and Neurodegeneration via Inhibiting Neuroinflammation Mediated by Nrf2/HO-1/NLRP3 Signaling in Status Epilepticus Model.

Studies on the bench and at bedside have demonstrated that the process of epileptogenesis is involved in neuroinflammatory responses. As the receptor of proinflammatory cytokine IL-1ß, IL-1ß type 1 receptor (IL-1R1) is reported to express abundantly in the endothelial cells in epileptic brains, which is deemed to be implicated in the epileptogenic process. However, whether and how endothelial IL-1R1 modulates neuroinflammatory responses in the pathological process of epileptic seizures and/or status epilepticus (SE) remains obscure. Here, we indicated endothelial IL-1R1 is involved in neuroinflammation, facilitating epilepsy progress via Nrf2/HO-1/NLRP3. In vitro, we observed upregulation of inflammatory cytokines in co-culture model under IL-1ß challenge, as well as in BV2 cells after stimulation with conditional medium (CM) from IL-1ß-stimulated bEnd.3 cells. In vivo, mice with conditional knockout of endothelial IL-1R1 (IL-1R1-CKO) were generated by hybrid IL-1R1flox/flox mice with Tek-Cre mice. IL-1R1-CKO reduced seizure susceptibility in kainic acid (KA)-induced SE model. In addition, IL-1R1-CKO KA mice exhibited lessened hippocampal neuroinflammation, mitigated neuronal damage, and decreased abnormal neurogenesis. In cognitive behavioral tests, IL-1R1-CKO KA mice presented improvement in learning and memory. Furthermore, we also indicated blockage of endothelial IL-1R1 downregulated the expressions of Nrf2/HO-1/NLRP3 pathway-related proteins. Nrf2-siRNA reversed the downregulation of HO-1, NLRP3, caspase-1, and IL-1ß. These results demonstrated CKO of endothelial IL-1R1 reduces seizure susceptibility and attenuates SE-related neurobehavioral damage by suppressing hippocampal neuroinflammation via Nrf2/HO-1/NLRP3.

Comprehensive characterization of MUC16 mutations in lung adenocarcinoma for immunotherapies and prognosis: An observational study.

Lung adenocarcinoma (LUAD) is a non-small-cell lung cancer and is the leading cause of cancer-related deaths worldwide. Immunotherapy is a promising candidate for LUAD, and tumor mutation burden (TMB) could be a new biomarker to monitor the response of cancer patients to immunotherapy. It is known that the mucin 16 (MUC16) mutation is the most common and affects the progression and prognosis of several cancers. However, whether MUC16 mutations are associated with TMB and tumor-infiltrating immune cells in LUAD is not fully elucidated. All the data were obtained from the cancer genome atlas database to assess the prognostic value and potential mechanism of MUC16 in LUAD. An immune prognostic model (IPM) was developed based on immune-related genes that could be differentially expressed between MUC16MUT and MUC16WT LUAD patients. Later, the IPM effect on the prognosis and immunotherapy of LUAD was comprehensively evaluated. MUC16 was frequently mutated in LUAD, with a mutational frequency of 43.4%, significantly associated with higher TMB and better clinical prognosis. Based on 436 patients with LUAD, an IPM was established and validated to differentiate patients with a low or high risk of poor survival. The univariate and multivariate Cox regression analyses demonstrated that the IPM was an independent prognostic indicator for LUAD patients. Elevated expressions of PD-L1, LAG3, PDCD1, and SIGLEC15, and most of the T-effector and interferon-γ gene signatures, were depicted in the high-risk group. Moreover, the nomogram using the IPM and clinical prognostic factors also predicted the overall survival and clinical utility. Our project developed a robust risk signature depending on the MUC16 status and provided novel insights for individualized treatment options for LUAD patients.

A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

BACKGROUND: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time. METHODS: ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens. The depth estimator network was developed on 7297 images from five virtually produced colon videos and tested on 730 images from seven virtual colon videos. The performance of the system was first evaluated in nine videos of a simulated colon with polyps attached, then tested in 157 real-world prospective videos from three hospitals, with the outcomes compared with that of nine endoscopists over 69 videos. Inappropriate surveillance recommendations caused by incorrect estimation of polyp size were also analyzed. RESULTS: The relative error of depth estimation was 11.3% (SD 6.0%) in successive virtual colon images. The concordance correlation coefficients (CCCs) between system estimation and ground truth were 0.89 and 0.93 in images of a simulated colon and multicenter videos of 157 polyps. The mean CCC of ENDOANGEL-CPS surpassed all endoscopists (0.89 vs. 0.41 [SD 0.29]; P<0.001). The relative accuracy of ENDOANGEL-CPS was significantly higher than that of endoscopists (89.9% vs. 54.7%; P<0.001). Regarding inappropriate surveillance recommendations, the system's error rate is also lower than that of endoscopists (1.5% vs. 16.6%; P<0.001). CONCLUSIONS: ENDOANGEL-CPS could potentially improve the accuracy of colorectal polyp size measurements and size-based surveillance intervals.

Development and Evaluation of a Surveillance System for Follow-Up After Colorectal Polypectomy.

Importance: The adherence of physicians and patients to published colorectal postpolypectomy surveillance guidelines varies greatly, and patient follow-up is critical but time consuming. Objectives: To evaluate the accuracy of an automatic surveillance (AS) system in identifying patients after polypectomy, assigning surveillance intervals for different risks of patients, and proactively following up with patients on time. Design, Setting, and Participants: In this diagnostic/prognostic study, endoscopic and pathological reports of 47 544 patients undergoing colonoscopy at 3 hospitals between January 1, 2017, and June 30, 2022, were collected to develop an AS system based on natural language processing. The performance of the AS system was fully evaluated in internal and external tests according to 5 guidelines worldwide and compared with that of physicians. A multireader, multicase (MRMC) trial was conducted to evaluate use of the AS system and physician guideline adherence, and prospective data were collected to evaluate the success rate in contacting patients and the association with reduced human workload. Data analysis was conducted from July to September 2022. Exposures: Assistance of the AS system. Main Outcomes and Measures: The accuracy of the system in identifying patients after polypectomy, stratifying patient risk levels, and assigning surveillance intervals in internal (Renmin Hospital of Wuhan University), external 1 (Wenzhou Central Hospital), and external 2 (The First People's Hospital of Yichang) test sets; the accuracy of physicians and their time burden with and without system assistance; and the rate of successfully informed patients of the system were evaluated. Results: Test sets for 16 106 patients undergoing colonoscopy (mean [SD] age, 51.90 [13.40] years; 7690 females [47.75%]) were evaluated. In internal, external 1, and external 2 test sets, the system had an overall accuracy of 99.91% (95% CI, 99.83%-99.95%), 99.54% (95% CI, 99.30%-99.70%), and 99.77% (95% CI, 99.41%-99.91%), respectively, for identifying types of patients and achieved an overall accuracy of at least 99.30% (95% CI, 98.67%-99.63%) in the internal test set, 98.89% (95% CI, 98.33%-99.27%) in external test set 1, and 98.56% (95% CI, 95.86%-99.51%) in external test set 2 for stratifying patient risk levels and assigning surveillance intervals according to 5 guidelines. The system was associated with increased mean (SD) accuracy among physicians vs no AS system in 105 patients (98.67% [1.28%] vs 78.10% [18.01%]; P = .04) in the MRMC trial. In a prospective trial, the AS system successfully informed 82 of 88 patients (93.18%) and was associated with reduced burden of follow-up time vs no AS system (0 vs 2.86 h). Conclusions and Relevance: This study found that an AS system was associated with improved adherence to guidelines among physicians and reduced workload among physicians and nurses.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS: The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION: The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction.

INTRODUCTION: There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process. RELATED WORK: Decision trees and their ensemble algorithms are considered to be suitable methods for pharmaceutical applications due to their excellent performance and good interpretability. We review existing decision trees or decision tree ensemble algorithms in the medical field and point out their shortcomings. METHOD: This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST). EXPERIMENT: The two-drug combination data sets are collected from two cell lines with three classes (additive, antagonistic and synergistic effects) to test our method. Experimental results show that both our DST and EDST perform better than other methods. Besides, the rules generated by our methods are more compact and more accurate than other rule-based algorithms. Finally, we also analyze the extracted knowledge by the model in the field of bioinformatics. CONCLUSION: The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

BACKGROUND: Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge. METHODS: ENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists. RESULTS: Forty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions. CONCLUSIONS: Great increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction.

Combination therapy is a promising clinical treatment strategy for cancer and other complex diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the therapeutic effect and slowing down drug resistance. To narrow the search space of synergistic drug combinations, many prediction models have been developed. However, drug combination datasets always have the characteristics of class imbalance. Synergistic drug combinations receive the most attention in clinical application but are in small numbers. To predict synergistic drug combinations in different cancer cell lines, in this study, we propose a genetic algorithm-based ensemble learning framework, GA-DRUG, to address the problems of class imbalance and high dimensionality of input data. The cell-line-specific gene expression profiles under drug perturbations are used to train GA-DRUG, which contains imbalanced data processing and the search of global optimal solutions. Compared to 11 state-of-the-art algorithms, GA-DRUG achieves the best performance and significantly improves the prediction performance in the minority class (Synergy). The ensemble framework can effectively correct the classification results of a single classifier. In addition, the cellular proliferation experiment performed on several previously unexplored drug combinations further confirms the predictive ability of GA-DRUG.

Human-Like Artificial Intelligent System for Predicting Invasion Depth of Esophageal Squamous Cell Carcinoma Using Magnifying Narrow-Band Imaging Endoscopy: A Retrospective Multicenter Study.

INTRODUCTION: Endoscopic evaluation is crucial for predicting the invasion depth of esophagus squamous cell carcinoma (ESCC) and selecting appropriate treatment strategies. Our study aimed to develop and validate an interpretable artificial intelligence-based invasion depth prediction system (AI-IDPS) for ESCC. METHODS: We reviewed the PubMed for eligible studies and collected potential visual feature indices associated with invasion depth. Multicenter data comprising 5,119 narrow-band imaging magnifying endoscopy images from 581 patients with ESCC were collected from 4 hospitals between April 2016 and November 2021. Thirteen models for feature extraction and 1 model for feature fitting were developed for AI-IDPS. The efficiency of AI-IDPS was evaluated on 196 images and 33 consecutively collected videos and compared with a pure deep learning model and performance of endoscopists. A crossover study and a questionnaire survey were conducted to investigate the system's impact on endoscopists' understanding of the AI predictions. RESULTS: AI-IDPS demonstrated the sensitivity, specificity, and accuracy of 85.7%, 86.3%, and 86.2% in image validation and 87.5%, 84%, and 84.9% in consecutively collected videos, respectively, for differentiating SM2-3 lesions. The pure deep learning model showed significantly lower sensitivity, specificity, and accuracy (83.7%, 52.1% and 60.0%, respectively). The endoscopists had significantly improved accuracy (from 79.7% to 84.9% on average, P = 0.03) and comparable sensitivity (from 37.5% to 55.4% on average, P = 0.27) and specificity (from 93.1% to 94.3% on average, P = 0.75) after AI-IDPS assistance. DISCUSSION: Based on domain knowledge, we developed an interpretable system for predicting ESCC invasion depth. The anthropopathic approach demonstrates the potential to outperform deep learning architecture in practice.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images.

Objective: In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload. Methods: A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance. Results: The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886. Conclusion: A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

The effectiveness of a computer-aided system in improving the detection rate of gastric neoplasm and early gastric cancer: study protocol for a multi-centre, randomized controlled trial.

BACKGROUND: This protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer (EGC) in routine oesophagogastroduodenoscopy (EGD). METHODS: Study design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial. SETTINGS: The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD. PARTICIPANTS: Adults who underwent screening, diagnosis, or surveillance EGD. Randomisation groups: 1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC; 2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC. RANDOMISATION: Block randomisation, stratified by centre. PRIMARY OUTCOMES: Detection rates of gastric neoplasms and EGC. SECONDARY OUTCOMES: Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD. BLINDING: Outcomes are undertaken by blinded assessors. SAMPLE SIZE: Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000. DISCUSSION: The results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings. TRIAL REGISTRATION: ChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.

Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy.

White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.

A hybrid deep forest-based method for predicting synergistic drug combinations.

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.