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Large language models:game-changers in the healthcare industry

science Bulletin 2025年第3期70卷 283-286页

作者： Bin Dong Li Zhang Jiajia Yuan Yang Chen Quanzheng Li Lin Shen Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing)Peking University Cancer Hospital and InstituteBeijing 100142China Massachusetts General Hospital Boston 02114-2696USA Harvard Medical School Boston 02115USA

The healthcare industry faces core challenges,including increasingly complex operational processes and entities,the rapid development of medical knowledge,and the rising demand for interdisciplinary *** complexity of medical processes is evident in every aspect,from patient appointments,diagnoses,and treatments to follow-up tasks,all of which involve intricate data processing and decision-making procedures.

关键词： industry expertise evident

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AI-DRIVEN AUTOMATED TOOL FOR ABDOMINAL CT BODY COMPOSITION ANALYSIS IN GASTROINTESTINAL CANCER MANAGEMENT

arXiv

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arXiv 2025年

作者： Nan, Xinyu He, Meng Chen, Zifan Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research Ministry of Education Peking University Cancer Hospital and Institute Beijing China Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

The incidence of gastrointestinal cancers remains significantly high, particularly in China, emphasizing the importance of accurate prognostic assessments and effective treatment strategies. research shows a strong correlation between abdominal muscle and fat tissue composition and patient outcomes. However, existing manual methods for analyzing abdominal tissue composition are time-consuming and costly, limiting clinical research scalability. To address these challenges, we developed an AI-driven tool for automated analysis of abdominal CT scans to effectively identify and segment muscle, subcutaneous fat, and visceral fat. Our tool integrates a multi-view localization model and a high-precision 2D nnUNet-based segmentation model, demonstrating a localization accuracy of 90% and a Dice Score Coefficient of 0.967 for segmentation. Furthermore, it features an interactive interface that allows clinicians to refine the segmentation results, ensuring high-quality outcomes effectively. Our tool offers a standardized method for effectively extracting critical abdominal tissues, potentially enhancing the management and treatment for gastrointestinal cancers. The code is available at https://***/NanXinyu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Diseases

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Ai-Driven Automated Tool for Abdominal CT Body Composition Analysis in Gastrointestinal Cancer Management

Ai-Driven Automated Tool for Abdominal CT Body Composition A...

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IEEE International Symposium on Biomedical Imaging

作者： Xinyu Nan Meng He Zifan Chen Bin Dong Lei Tang Li Zhang Center for Data Science Peking University China Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education) Peking University Cancer Hospital and Institute Beijing China Beijing International Center for Mathematical Research (BICMR) Peking University Beijing China Center for Machine Learning Research Peking University Beijing China National Biomedical Imaging Center Peking University Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

关键词： Location awareness Analytical models Accuracy Computed tomography Scalability Manuals Muscles Gastrointestinal tract Fats Cancer

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B-type natriuretic peptide efficacy compared to fragmented QRS for diastolic dysfunction screening in patients with type 2 diabetes

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World Journal of Diabetes 2025年第4期16卷 84-95页

作者： Kunimasa Yagi Daisuke Chujo Isao Usui Jian-Hui Liu Atsushi Nohara Asako Enkaku Shirozu Akiko Takikawa Hisae Honoki Shiho Fujisaka Hideki Origasa Hayato Tada Department of Internal Medicine Kanazawa Medical University HospitalKahoku 920-0293IshikawaJapan Center for Clinical and Translational Research Toyama University HospitalToyama 930-0152ToyamaJapan Department of Endocrinology and Metabolism Dokkyo Medical UniversityUtsunomiya 321-0293TochigiJapan Department of Cardiology Ningbo Medical Center of Lihuili HospitalNingbo 315041Zhejiang ProvinceChina Department of Clinical Genetics Ishikawa Prefectural Central HospitalKanazawa 920-8530IshikawaJapan The First Department of Internal Medicine Toyama University HospitalToyama 930-0152ToyamaJapan Data Science and AI Innovation Research Promotion Center Institute of Statistical MathematicsShiga UniversityHikone 525-0034ShigaJapan Division of Cardiovascular Medicine Kanazawa UniversityGraduate School of MedicineKanazawa 920-8640IshikawaJapan

BACKGROUND Early diagnosis of left ventricular diastolic dysfunction(LVDD)is essential for preventing heart failure.B-type natriuretic peptide(BNP)is a viable marker for predicting LVDD,as elevated BNP levels have been associated with worsening LVDD in patients with diabetes over ***,the utility of BNP as a diagnostic marker in diabetes is controversial,as BNP levels are often low in overweight *** To examine the effectiveness of BNP levels and fragmented QRS(fQRS)on electrocardiography for diagnosing LVDD in patients with type 2 *** This retrospective cohort study included 303 patients with type 2 diabetes(67.4±12.3 years old)with preserved ejection fraction(EF)≥50%admitted to Toyama University Hospital for glycemic management and comorbidity evaluation between November 2017 and April *** participants underwent plasma BNP measurement,electrocardiography,and *** who were blinded to the BNP results assessed the electrocardiograms and *** analyses were conducted for overweight *** Receiver operating characteristic(ROC)curve analysis determined optimal BNP cut-off values of 34.8 pg/mL and 7.2 pg/mL for diagnosing LVDD in non-overweight[area under the ROC curve(AUC):0.70]and overweight(AUC:0.55)groups,respectively(P=0.040).In the overweight subgroup,fQRS showed greater diagnostic accuracy for LVDD(AUC:0.67),indicating moderate diagnostic utility compared with the low performance of the BNP cutoff of 35 pg/mL(AUC:0.52)(P=0.010).Multivariate analyses confirmed that fQRS was superior to BNP for LVDD diagnosis regardless of the patient’s *** A BNP level≥35 pg/mL in non-overweight individuals may be a reliable LVDD ***,fQRS was more effective than BNP in diagnosing LVDD irrespective of the patient’s *** can complement BNP in the early detection of LVDD,especially in overweight patients,potentially improving early detection and mitigatin

关键词： B-type natriuretic peptide Diastolic dysfunction Fragmented QRS Heart failure with preserved ejection fraction Overweight Type 2 diabetes

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THIDS: Triplet High-Order Individual Differential Structural Brain Network

THIDS: Triplet High-Order Individual Differential Structural...

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IEEE International Symposium on Biomedical Imaging

作者： Dailin Wen Chunzhi Zhao Tulay Adali Daoqiang Zhang Vince D. Calhoun Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Department of CSEE University of Maryland Baltimore MD USA Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

The human brain is a highly integrated network, and the brain network models serve as a simplified representation of this complex system. However, traditional brain network approaches are limited to group-level mean comparisons and first-order interactions, failing to capture the individual differences with higher-order interactions among brain regions in both brain development and heterogeneous psychiatric disorders. Here, we propose a triplet high-order individual differential structural brain network (THIDS), which is a linear combination of the differential (reference-perturbed) and perturbed networks, while utilizing the interaction information among all triplets of brain regions to construct an individual difference structured brain network. Results show that THIDS consistently achieves the highest accuracy in schizophrenia diagnosis, gender classification and cognition prediction across two datasets, four classification models and seven cognitive domains, compared with four alternative brain network construction methods. These results demonstrate that the proposed THIDS approach reliably captures individual differential features in the brain, showing great potential to investigate especially heterogeneous brain disorders and individual brain development.

关键词： Biometrics Accuracy Perturbation methods Schizophrenia Predictive models Brain modeling Cognition Robustness Complex systems Biomedical imaging

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Single-cell technologies: current and near future

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science China(Life sciences) 2025年第1期68卷 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi KeyLaboratory of Spine and Spinal CordInjury Repairand Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchoolofLifeSciencesandTechnologyTongji UniversityShanghai 200082China Frontier Science Center for Stem Cells School of Life Sciences and TechnologyTongji UniversityShanghai 200092China Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji UniversityShanghai 200082China Research Institute of Intelligent Computing Zhejiang LabHangzhou 311121China Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314103China Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) School of StatisticsEast China Normal UniversityShanghai 200062China

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity,tissue organization,and overall organism function(Sun et al.,2024).In 2009,single-cell RNA sequencing(scRNA-seq)was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology,single-cell approaches have expanded to encompass other omics,such as genomics,epigenomics,proteomics,and ***,multimodal technologies including spatially resolved transcriptomics and clustered regularly interspaced short palindromic repeats(CRISPR)screening at the single-cell level further advanced our ability to comprehensively understand cellular ***-cell methods provide deeper insights into disease mechanisms across various conditions,including cancer,developmental disorders,and aging-associated *** this issue,we focus on topics related to single-cell methods,covering the following four aspects:(i)single-cell multi-omic sequencing technology;(ii)single-cell spatial technology;(iii)singlecell CRISPR screening technology;(iv)applications of single-cell technology.

关键词： expanded enable insight

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Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data

arXiv

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arXiv 2025年

作者： Geenjaar, Eloy Calhoun, Vince Georgia Institute of Technology United States Translational Research in Neuroimaging & Data Science Center Georgia State University Georgia Institute of Technology Emory University United States

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is key to understanding individual differences in brain function, yet most existing machine learning methods either rely on population-level spatial alignment or assume data that is temporally structured, either because data is aligned among subjects or because event timings are known. We introduce a manifold learning framework that can capture subject-specific spatial variations across both structured and temporally unstructured neuroimaging data. On simulated data and two naturalistic fMRI datasets (Sherlock and Forrest Gump), our framework outperforms group-based baselines by recovering more accurate and individualized representations. We further show that the framework scales efficiently to large datasets and generalizes well to new subjects. To test this, we apply the framework to temporally unstructured resting-state fMRI data from individuals with schizophrenia and healthy controls. We further apply our method to a large resting-state fMRI dataset comprising individuals with schizophrenia and controls. In this setting, we demonstrate that the framework scales efficiently to large populations and generalizes robustly to unseen subjects. The learned subject-specific spatial maps our model finds reveal clinically relevant patterns, including increased activation in the basal ganglia, visual, auditory, and somatosensory regions, and decreased activation in the insula, inferior frontal gyrus, and angular gyrus. These findings suggest that our framework can uncover clinically relevant subject-specific brain activity patterns. Our approach thus provides a scalable and individualized framework for modeling brain activity, with applications in computational neuroscience and clinical research. © 2025, CC BY.

关键词： Brain mapping

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

Longitudinal Segmentation of MS Lesions via Temporal Diffe...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031845246

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/Longitudinal-Difference-Weighting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Incidence and risk factors of mental illnesses among patients with systemic autoimmune rheumatic diseases: an 18-year population-based study

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Rheumatology (Oxford, England) 2025年第3期64卷 976-984页

作者： Chu, Wei-Min Chao, Wen-Cheng Chen, Der-Yuan Ho, Wei-Li Chen, Hsin-Hua Department of Family Medicine Taichung Veterans General Hospital Taichung Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Geriatrics and Gerontology Research Center College of Medicine National Chung Hsing University Taichung Taiwan Department of Epidemiology on Aging National Center for Geriatrics and Gerontology Aichi Japan Department of Critical Care Medicine Taichung Veterans General Hospital Taichung Taiwan Big Data Center National Chung Hsing University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Deparment of Rheumatology and Immunology China Medical University Hospital Taichung Taiwan College of Medicine China Medical University Taichung Taiwan Division of Allergy Immunology and Rheumatology Taichung Veterans General Hospital Chiayi Branch Chiayi Taiwan Division of General Medicine Division of Allergy Immunology and Rheumatology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Institute of Biomedical Science and Rong Hsing Research Center for Translational Medicine & Big Data Center Chung Hsing University Taichung Taiwan

OBJECTIVE: This study aimed to assess the incidence and risk factors surrounding mental illnesses in patients diagnosed with systemic autoimmune rheumatic diseases (SARDs). METHODS: This retrospective cohort study used nationwide, population-based claim data taken from Taiwan's National Health Insurance research database (NHIRD) to identify patients certified as having a catastrophic illness for systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSc), dermatomyositis (DM), polymyositis (PM) or Sjögren's syndrome (SS) from the years 2002-2020. We furthermore calculated the incidence of mental illness in patients diagnosed with SARDs while exploring factors associated with the development of mental illness using multivariable Cox regression analysis shown as adjusted hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Among the 28 588 participants, the average age was 47.4 (SD 14.9) years, with most participants being female (76.4%). When compared with patients with rheumatoid arthritis, patients with SLE (HR: 1.20, 95% CI: 1.10-1.32), SS (HR: 1.29, 95% CI: 1.19-1.39), and DM (HR: 1.28, 95% CI: 1.04-1.32) showed a significantly increased risk of developing mental illness. Additionally, when compared with patients with rheumatoid arthritis, patients with SLE (HR: 1.32, 95% CI: 1.21-1.44), SSc (HR: 1.20, 95% CI: 1.02-1.41), SS (HR: 1.17, 95% CI: 1.08-1.26), DM (HR: 1.73, 95% CI: 1.44-2.07), and PM (HR: 1.64, 95% CI: 1.32-2.03) showed a significantly increased risk of antidepressant use. CONCLUSION: This population-based cohort study revealed that patients diagnosed with SLE, SS, and DM had significantly higher risks of developing mental illness when compared with patients with RA. © The Author(s) 2024. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For commercial re-use, please contact reprints@*** for reprints and translation rights for reprints. All other pe

关键词： incidence mental illness population-based study rheumatic disease

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Germline Pathogenic Variants and Clinical Outcomes in Asian Patients With Breast Cancer

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Cancer science 2025年第4期116卷 1048-1058页

作者： Kitadai, Rui Yazaki, Shu Kuchiba, Aya Yamanaka, Takashi Shiino, Sho Yamauchi, Chisako Harano, Kenichi Saito, Motonobu Hirotsu, Yosuke Aiba, Hisaki Yoshida, Teruhiko Hamamoto, Ryuji Shimizu, Chikako Shimomura, Akihiko Kojima, Yuki Shimoi, Tatsunori Momozawa, Yukihide Sudo, Kazuki Yoshida, Masayuki Sunami, Kuniko Hori, Megumi Katanoda, Kota Shimada, Yoko Yamashita, Yuji Kogawa, Takahiro Murata, Takeshi Fujiwara, Saori Miyagi, Yohei Nakagomi, Hiroshi Tachibana, Kazunoshin Omata, Masao Ohtake, Tohru Suto, Akihiko Onishi, Tatsuya Naito, Yoichi Yamashita, Toshinari Yonemori, Kan Kohno, Takashi Shiraishi, Kouya Division of Genome Biology National Cancer Center Research Institute Tokyo Japan Department of Medical Oncology National Cancer Center Hospital Tokyo Japan Department of International Clinical Development National Cancer Center Hospital Tokyo Japan Teikyo University Graduate School of Public Health Tokyo Japan Biostatistics Division Center for Research Administration and Support/Division of Biostatistical Research Institute for Cancer Control National Cancer Center Tokyo Japan Department of Breast Surgery and Oncology Kanagawa Cancer Center Yokohama Japan Department of Breast Surgery National Cancer Center Hospital Tokyo Japan Department of Breast Surgery National Cancer Center Hospital East Chiba Japan Department of Experimental Therapeutics National Cancer Center Hospital East Chiba Japan Department of Medical Oncology National Cancer Center Hospital East Chiba Japan Department of Gastrointestinal Tract Surgery Fukushima Medical University School of Medicine Fukushima Japan Genome Analysis Center Yamanashi Central Hospital Yamanashi Japan Department of Genetic Medicine and Services National Cancer Center Hospital Tokyo Japan Division of Medical AI Research and Development National Cancer Center Research Institute Tokyo Japan Cancer Translational Research Team RIKEN Center for Advanced Intelligence Project Tokyo Japan Department of Breast and Medical Oncology National Center for Global Health and Medicine Tokyo Japan Laboratory for Genotyping Development Center for Integrative Medical Sciences Yokohama Japan Department of Diagnostic Pathology National Cancer Center Hospital Tokyo Japan School of Nursing University of Shizuoka Shizuoka Japan Division of Population Data Science National Cancer Center Institute for Cancer Control Tokyo Japan Division of Early Clinical Development for Cancer Department of Advanced Medical Development The Cancer Institute Hospital of Japanese Foundation for Cancer Research Tokyo Japan Molecular Pathology and Genetics Di

Despite the importance of genetic testing for risk assessment and treatment in breast cancer, the prognostic impact of germline pathogenic variants (PVs), especially in Asian populations, is unclear. We assessed the impact of germline PVs in patients with early-stage breast cancer. This study included 7278 Japanese multihospital registry patients. PVs of ATM, BRCA1, BRCA2, CDH1, CHEK2, NBN, NF1, PALB2, PTEN, STK11, and TP53 were evaluated. PV and non-PV carriers were matched by age, histology, and stage. Associations between PVs and survival were assessed. The primary outcome was invasive disease-free survival (IDFS). Secondary outcomes included relapse-free survival (RFS), overall survival, and breast cancer-specific survival. We identified 320 (4.4%) patients with BRCA1/2 PVs and 79 (1.1%) with PVs other than BRCA1/2 (non-BRCA1/2). A total of 360 patients (BRCA1/2, n = 289;non-BRCA1/2, n = 71) were matched to 720 noncarriers. Patients with BRCA1/2 PVs had significantly shorter 10-year IDFS (adjusted hazard ratio (aHR) = 2.15;95% confidence interval (CI), 1.61–2.86;p < 0.001);and RFS (aHR = 1.74;95% CI, 1.25–2.44;p = 0.001) than noncarriers. Among patients with hormone receptor-positive HER2-negative breast cancer, BRCA1/2 PV carriers exhibited significantly shorter 10-year IDFS than noncarriers, even those with stage I/II disease (total, aHR = 2.23;95% CI, 1.55–3.23;p < 0.001, Stage I/II, aHR = 2.22;95% CI, 1.43–3.44;p < 0.001). There was no significant difference in 10-year IDFS between the non-BRCA1/2 PV carrier and noncarrier groups (aHR = 1.40;95% CI, 0.67–2.93;p = 0.37). Asian patients with breast cancer carrying germline BRCA1/2 PV, even those with a low recurrence risk, have significantly shorter 10-year IDFS than noncarriers. © 2025 The Author(s). Cancer science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

关键词： Breast cancer cancer predisposition genes hormone receptor-positive pathogenic variants prognosis

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