AI Medical Compendium

Integrating Reproductive and Clinical Variables to Predict Postpartum Disability Outcomes in Multiple Sclerosis Using Machine Learning.

Multiple sclerosis and related disorders Mar 2, 2026

BACKGROUND: Pregnancy represents a unique immunological state in women with multiple sclerosis (MS), and postpartum disease reactivation is a major concern. While pregnancy outcomes have been extensively described, the long-term effects of reproducti... read more

Vessel-specific perivascular fat attenuation index derived from AI-CCTA and its association with impaired coronary flow reserve in patients with INOCA.

International journal of cardiology. Heart & vasculature Mar 2, 2026

OBJECTIVES: To investigate the association between artificial intelligence (AI)-derived coronary computed tomography angiography (CCTA) features and impaired coronary flow reserve (CFR) in patients with ischemia and non-obstructive coronary arteries ... read more

Differentiating unipolar and bipolar depression using multi-task eye-movement features.

Psychiatry research Mar 2, 2026

BACKGROUND: Differentiating bipolar depression (BPD) from unipolar depression (UPD) is clinically challenging due to symptom overlap. This study explores eye-movement differences between UPD, BPD, and healthy controls (HCs) using a multi-task eye-tra... read more

A LiDAR-based machine vision dataset for online volume measurement of sweetpotatoes.

Data in brief Mar 2, 2026

Volume is an important shape descriptor in postharvest quality evaluation and breeding programs of sweetpotatoes and is also valuable for other agricultural engineering applications. Traditional volume measurement methods based on water displacement ... read more

AutoPET Challenge on Fully Automated Lesion Segmentation in Oncologic PET/CT Imaging, Part 2: Domain Generalization.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Mar 2, 2026

This article reports the results of the second iteration of the autoPET challenge on automated lesion segmentation in whole-body PET/CT, held in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Inter... read more

Deep Learning-Derived Sarcopenia Marker Predicts Benefit from Anti-EGFR Therapy in Patients with RAS Wild-type Metastatic Colorectal Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research Mar 2, 2026

PURPOSE: The benefit of treatment intensification in metastatic colorectal cancer (mCRC) may be influenced by host-related factors that are not accounted for in clinical trials or standard care. We investigated the prognostic and predictive value of ... read more

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Physics in medicine and biology Mar 2, 2026

Objective.While photon-counting computed tomography (PCCT) improves image quality and reduces radiation dose, artifacts induced by cardiac and respiratory motion is still a challenge. The purpose of this work is to evaluate the potential of an image-... read more

Distinct Tumor-Immune Ecologies in Patients with Lung Cancer Predict Progression and Define a Clinical Biomarker of Therapy Response.

Cancer research Mar 2, 2026

UNLABELLED: Multiplexed imaging of tissues is an approach that holds promise for improving early detection, diagnosis, and treatment of cancer. In this study, we investigated multiplexed histologic images of paired pretreatment and on-treatment sampl... read more

Machine learning approaches to optimize the integration of sociodemographic factors for predicting cancer-specific survival among patients with high-risk prostate cancer.

Current urology Mar 2, 2026

BACKGROUND: Sociodemographic factors influence the outcomes of prostate cancer (PCa); however, they are rarely incorporated into clinical risk prediction models. This study aimed to assess whether machine learning approaches could optimize the integr... read more

Sparse Autoencoders Reveal Interpretable Features in Single-Cell Foundation Models

bioRxiv Mar 2, 2026

Single-cell foundation models (scFMs) hold promise for applications in cell type annotation, data integration, and prediction of the effects of cell perturbations, but their internal mechanisms remain poorly understood. We investigate the structure o... read more

bioinformatics

Artificial Intelligence Medical Compendium