Shanghai Jiao Tong University

10 related articles

researchsquare.com Oct 20, 2026
Rotating sound sources located without propagation functions: 16 microphones, a prediction in under 50 milliseconds
Rotating sound source localization has long been troubled by ghost sources: time-domain ROSI and frequency-domain MCB both fail to remove Doppler-induced spurious sources when array geometry is unconstrained. A ResNet network takes an MCB-derived cross-spectral matrix as input and outputs the source intensity distribution directly, reaching up to 98.83% localization accuracy in simulations with 16 microphones, 500 to 2500 rpm and 4500 to 5000 Hz, with inference within 50 milliseconds per case.
Rotating sound source localization has long been troubled by ghost sources: time-domain ROSI and frequency-domain MCB both fail to remove Doppler-induced spurious sources when array geometry is unconstrained. A ResNet network takes an MCB-derived cross-spectral matrix as input and outputs the source intensity distribution directly, reaching up to 98.83% localization accuracy in simulations with 16 microphones, 500 to 2500 rpm and 4500 to 5000 Hz, with inference within 50 milliseconds per case.
Shanghai Jiao Tong University Aero Engine Corporation of China (China)Ce Zhang Yifeng Yao Cheng Wei Lee Wei Ma
Residual Neural Network Rotating Sound Source Localization Mode Composition Beamforming
medRxiv Oct 1, 2026
StrokeAgent matched expert final decisions in 86% of 100 acute stroke cases, exceeding tool-free LLMs and neurologists
The study presents StrokeAgent, a multimodal agentic decision-support framework built around temporal alignment and continuous information synthesis that processes clinical records, laboratory results, raw electrocardiographic (ECG) waveforms and source multimodal CT images to support emergency triage, the initial thrombolysis decision and final reperfusion planning, and in a curated set of 100 patients with multimodal clinical and imaging data enriched for clinically complex reperfusion scenarios it achieved 86% concordance with expert-adjudicated final decisions, exceeding the mean of three tool-free large language models by 13 percentage points and the mean of six resident and attending neurologists by 17 percentage points, with 76% complete pathway concordance, completing the full deci
The study presents StrokeAgent, a multimodal agentic decision-support framework built around temporal alignment and continuous information synthesis that processes clinical records, laboratory results, raw electrocardiographic (ECG) waveforms and source multimodal CT images to support emergency triage, the initial thrombolysis decision and final reperfusion planning, and in a curated set of 100 patients with multimodal clinical and imaging data enriched for clinically complex reperfusion scenarios it achieved 86% concordance with expert-adjudicated final decisions, exceeding the mean of three tool-free large language models by 13 percentage points and the mean of six resident and attending neurologists by 17 percentage points, with 76% complete pathway concordance, completing the full deci
Zhejiang University Fudan University Shanghai Innovation Institute University of Macau Northwestern Polytechnical University Shanghai Medical College of Fudan University Shanghai Jiao Tong University Zhongshan Hospital Zhuohang Liu Jiajie Peng Zhijie Bao Xiaoyi Xue Rong Ji Zhongyu Wei Xuan Shi Yuxiao Chen
Multimodal Agentic Decision Support Clinical Decision Support Emergency Triage Prediction
The FASEB Journal Sep 30, 2026
Integrating machine learning with multilayer transcriptomics pins JAK2 and ANXA5 as key genes linking obstructive sleep apnea to oxidative stress, validated in patient adipose tissue, intermittent-hypoxia mice, and post-CPAP samples
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
Combining limma differential analysis, WGCNA, a GeneCards oxidative-stress gene set, PPI networks, and three machine learning methods (LASSO, random forest, SVM-RFE), the study narrowed obstructive sleep apnea (OSA) adipose transcriptomes to 57 shared differentially expressed genes and two hub genes, JAK2 and ANXA5, then used single-cell sequencing, scTenifoldKnk virtual knockout, immune deconvolution, RT-qPCR, and Western blotting to show that JAK2 is significantly upregulated and ANXA5 significantly downregulated in OSA, that both are enriched in monocytes, and that CPAP treatment lowers JAK2 while raising ANXA5.
Shanghai Jiao Tong University XinHua Hospital Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases Shanghai Children's Hospital Jin Xie Yuxuan Wu Penghui Chen Yifei Zhang Bin Li
CSIAM Transactions on Applied Mathematics Sep 29, 2026
Chen, Ji and Xu propose the DiGCA phase classifier, generating Lifshitz-Petrich phase diagrams about two orders of magnitude faster with over 98% classification accuracy
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
The authors propose a Derivative-informed Graph Convolutional Autoencoder (DiGCA) phase classifier that feeds both the Lifshitz-Petrich model solutions and their derivatives (the nonlocal term G(φ)) into a graph convolutional autoencoder for dimensionality reduction, then classifies with a fully connected neural network, generating phase diagrams over the parameter domain [−0.01,0.05]×[0,1] with over 98% classification accuracy, roughly two orders of magnitude faster than MCMS-RBM, and remaining stable under up to 10% additive white noise.
University of Massachusetts Dartmouth Shanghai Jiao Tong University Yale University Zhenli Xu Yanlai Chen
Lifshitz-Petrich Model Phase Classification Autoencoder Reduced-Order Modeling Graph Neural Network
Frontiers in Oncology Sep 28, 2026
Network meta-analysis of 10 phase III trials and over 8,000 advanced gastric cancer patients ranks trastuzumab and tislelizumab highest for delaying quality-of-life deterioration
This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
This network meta-analysis pooled 10 phase III randomized controlled trials with over 8,000 patients with unresectable advanced gastric cancer, ranked first-line immune checkpoint inhibitors and targeted agents by SUCRA on time to deterioration across EORTC QLQ-C30, EORTC QLQ-STO22 and EQ-5D domains, and used an exploratory minimum distance criterion to integrate overall survival with health-related quality of life, finding that trastuzumab (HER2-positive population) and tislelizumab (largely biomarker-unselected population) ranked most favorably across most quality-of-life domains, while the composite metric remains exploratory, lacks uncertainty estimates, and should not serve as primary evidence for clinical decision-making.
Shanghai Jiao Tong University Renji Hospital First Affiliated Hospital of Soochow University Soochow University Xiping Shen Yefeng Wu Wu Ji Zhigang Zheng Huijuan Dai
Surface Under the Cumulative Ranking Curve Network Meta-analysis Advanced Gastric Cancer Health-Related Quality of Life
arXiv Sep 26, 2026
SCISSR swaps point and box prompts for scribbles, reaching 95.41% Dice on EndoVis 2018 and 96.30% Dice on cross-domain CholecSeg8k
The work presents SCISSR, a scribble-promptable framework for interactive surgical scene segmentation: a lightweight Scribble Encoder turns freehand scribbles into dense prompt embeddings compatible with the mask decoder, and together with Spatial Gated Fusion and toggleable LoRA adapters it supports multi-round correction over a frozen SAM 2 backbone, reaching 95.41% Dice on EndoVis 2018 with five interaction rounds and 96.30% Dice on the unseen CholecSeg8k with three rounds, outperforming iterative point prompting on both benchmarks.
The work presents SCISSR, a scribble-promptable framework for interactive surgical scene segmentation: a lightweight Scribble Encoder turns freehand scribbles into dense prompt embeddings compatible with the mask decoder, and together with Spatial Gated Fusion and toggleable LoRA adapters it supports multi-round correction over a frozen SAM 2 backbone, reaching 95.41% Dice on EndoVis 2018 with five interaction rounds and 96.30% Dice on the unseen CholecSeg8k with three rounds, outperforming iterative point prompting on both benchmarks.
University of Michigan Shanghai Jiao Tong University Cheng Yuan Jian Jiang Lv Wu Qizhen Sun Yutong Ban
Advanced Neural Network Applications Surgical Simulation and Training Multimodal Machine Learning Applications
EuroIntervention Sep 21, 2026
Radial wall strain for residual risk stratification after percutaneous coronary intervention
Using the TARGET All Comers randomised trial of 1,551 patients, this study developed an artificial intelligence algorithm for real-time automated assessment of radial wall strain (RWS) from routine angiography and evaluated its prognostic value in 1,384 non-target vessels from 802 patients, finding that baseline maximal RWS (RWSmax) ≥13% independently predicted the 5-year non-target vessel-oriented composite endpoint (NT-VOCE) with an adjusted hazard ratio of 4.82 (95% CI 3.14-7.40) and an adjusted AUC of 0.73, with the highest predictive accuracy for non-target vessel revascularisation (adjusted AUC 0.92) and even greater performance in the high-quality angiographic image subgroup (adjusted HR 6.89).
Using the TARGET All Comers randomised trial of 1,551 patients, this study developed an artificial intelligence algorithm for real-time automated assessment of radial wall strain (RWS) from routine angiography and evaluated its prognostic value in 1,384 non-target vessels from 802 patients, finding that baseline maximal RWS (RWSmax) ≥13% independently predicted the 5-year non-target vessel-oriented composite endpoint (NT-VOCE) with an adjusted hazard ratio of 4.82 (95% CI 3.14-7.40) and an adjusted AUC of 0.73, with the highest predictive accuracy for non-target vessel revascularisation (adjusted AUC 0.92) and even greater performance in the high-quality angiographic image subgroup (adjusted HR 6.89).
St Bartholomew's Hospital Azienda Ospedaliera Universitaria Integrata Verona Huadong Hospital Fudan University Shanghai Jiao Tong University Queen Mary University of London University of Verona Ollscoil na Gaillimhe – University of Galway William Wijns Zhiqing Wang Jiaxin Zhong on behalf of the SMART-CHOICE three investigators Haocheng Huang Andreas Baumbach Helen Parise Alexandra J. Lansky
AI-based angiographic radial wall strain assessment Plaque vulnerability risk stratification
medRxiv Sep 16, 2026
Preoperative Social Connection and Postoperative Outcomes in Adults Undergoing Surgery: A Systematic Review and Meta-analysis
This systematic review and meta-analysis of 445 studies found that weaker preoperative social connection was associated with early postoperative mortality (OR 1.50, 95% CI 1.12-2.01) and non-home discharge (OR 1.95, 95% CI 1.35-2.81), while confidence intervals for postoperative survival, unplanned readmission, and complications all included 1, with overall certainty ranging from low to very low.
Shanghai Ninth People's Hospital Shanghai Jiao Tong University Hong Kong Metropolitan University Peking University Third Hospital Long Jiang Youhao Wang Zehao Jing Xihao Huang Zhicheng Zhang Chuan Yin
Journal of Imaging Informatics in Medicine Sep 15, 2026
Longitudinal Opportunistic T12 CT-BMD Assessment from Serial Health-Checkup Chest CT in Midlife Women
In this single-center retrospective study of 1,034 women aged 45-65 undergoing health checkups, 4,185 serial noncontrast chest CT examinations were used to derive AI-quantified T11 and T12 bone mineral density and build individual longitudinal trajectories, showing that women with low baseline bone status defined as mean T11-T12 AI-derived CT-BMD <= 128 mg/cm3 had smaller absolute BMD loss (10.82 vs 12.52 mg/cm3; P = 0.022) and a less negative T12 slope (-3.08 vs -3.91 mg/cm3/year; P = 0.011), while the greater relative decline seen in unadjusted analysis was not sustained after covariate adjustment or T11-only sensitivity analysis, and adding T11 BMD to clinical variables gave no clear incremental discrimination for rapid loss (AUC 0.708 vs 0.716; P = 0.128).
In this single-center retrospective study of 1,034 women aged 45-65 undergoing health checkups, 4,185 serial noncontrast chest CT examinations were used to derive AI-quantified T11 and T12 bone mineral density and build individual longitudinal trajectories, showing that women with low baseline bone status defined as mean T11-T12 AI-derived CT-BMD <= 128 mg/cm3 had smaller absolute BMD loss (10.82 vs 12.52 mg/cm3; P = 0.022) and a less negative T12 slope (-3.08 vs -3.91 mg/cm3/year; P = 0.011), while the greater relative decline seen in unadjusted analysis was not sustained after covariate adjustment or T11-only sensitivity analysis, and adding T11 BMD to clinical variables gave no clear incremental discrimination for rapid loss (AUC 0.708 vs 0.716; P = 0.128).
Huadong Hospital Fudan University Shanghai Jiao Tong University Shanghai Chest Hospital Huashan Hospital Zongjing Ma Wei Hong Minhao Liang Ming Li Liang Jin
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Sep 15, 2026
ENDOVISTA-ENT: Development and Preliminary Validation of an Integrated Artificial Intelligence System for Quality-Control Assistance and Lesion Recognition During Nasopharyngolaryngoscopy
In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.
In this retrospective two-center study of 2 365 patients (1 562 from the First Affiliated Hospital of Sun Yat-sen University and 803 from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine), the authors developed ENDOVISTA-ENT, an integrated AI system with Model 1 for inside/outside-body image determination, Model 2 for recognition of 11 standard anatomical sites, and Model 3 for lesion localization and benign/malignant classification, reporting internal/external accuracies of 99.44%/99.84% and 96.09%/94.73%, malignant-lesion AUCs of 0.986/0.968, early nasopharyngeal, laryngeal, and hypopharyngeal cancer AUCs of 0.878-0.932, an increase in six physicians' overall interpretation accuracy on 200 pathologically confirmed cases from 78.50% to 88.20% with AI assistance (χ²=40.
Sun Yat-sen University Ruijin Hospital Shanghai Jiao Tong University The First Affiliated Hospital, Sun Yat-sen University Wen-Ting Lei B Ye X J Li Y Y Li W Q Chen B Liu

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