arXiv Machine Learning By Diana Shadibaeva, Rochak Dhakal, Kui Zhang, Xiaofeng Yang, Saurabh Malhotra, Weihua Zhou

Artificial Intelligence Across the Cardiac Amyloidosis Diagnostic Pathway: From Single-Modality Detection to Multimodal Clinical Integration

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arXiv:2607. 09948v1 Announce Type: cross Abstract: Cardiac amyloidosis (CA) is increasingly recognized but remains substantially underdiagnosed, because its clinical and imaging phenotype overlaps with more common cardiomyopathies.

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arXiv Machine Learning
Sep 22

ORION-CMR: On-scanner Reporting with Integrated Foundation Model for End-to-End Cardiac MRI Analysis and Interpretation

ORION‑CMR is a scanner‑native, end‑to‑end foundation model for cardiac MRI that performs sequence classification, ventricular function assessment, LGE detection, disease classification, and generates reports in about 90 seconds. Trained on 12.9 million images, it outperformed supervised baselines and a prior CMR foundation model, achieving state‑of‑the‑art LGE classification and scar segmentation. In a multi‑vendor clinical cohort, it reached an AUC of 0.96 for normal‑vs‑abnormal detection and 0.88 for multiclass disease classification, with generated reports agreeing 81.4% with expert interpretation.

By Omer Burak Demirel, Kelly K. Horst, Alessio Perazzolo, Elisa Bruno, Kenan Kaya, Rongzhen Ouyang, Enas Ahmed, Jouke Smink, Spencer L. Waddle, Zainudeen Kallumpurath, Tzu Cheng Chao, Dinghui Wang, Steve G. Langer, Timothy L. Kline, Panagiotis Korfiatis, Jacinta Browne, Ivana Isgum, Tim Leiner
arXiv Machine Learning
Jul 20

CardioMeta: Calibrated Multi-Task Prediction of Diabetes, Hypertension, and Cardiovascular Disease Across Population and EHR Data

arXiv:2607. 15721v1 Announce Type: new Abstract: Cardiometabolic diseases remain among the most persistent drivers of preventable morbidity because diabetes, hypertension, and cardiovascular disease frequently co-occur and share metabolic, vascular, demographic, and behavioral determinants.

By S M Asif Hossain, Ruksat Khan Shayoni, M. F. Mridha, Jungpil Shin
arXiv AI
Jun 2

CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations

arXiv:2606. 00123v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance on public medical benchmarks, yet existing evaluations often remain weak proxies for clinical use, relying on isolated inputs and simplified recognition-style tasks.

By Zixian Su, Hongkai Zhang, Fan Gao, Encheng Su, Taiping Qu, Jingwei Guo, Nan Zhang, Hui Wang, Zhen Zhou, Kairui Bo, Yan Chen, Yue Ren, Shuai Li, Lei Xu, Henggui Zhang
arXiv AI
Aug 3

Technological Advances in Detecting and Managing Cognitive Impairment in Older Adults: Trends, Challenges, and Future Directions

arXiv:2607. 28687v1 Announce Type: cross Abstract: As populations age, cognitive decline from mild cognitive impairment (MCI) to dementia is a defining health challenge of the coming decades, yet routine assessment often misses its earliest signs.

By Mohammad Asif, Azizuddin Khan, Mohd Azam, Anurag Rajkumar Bombarde
arXiv Machine Learning
Jun 9

Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder

arXiv:2412. 06147v2 Announce Type: replace Abstract: For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role.

By Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi