AI Medical Compendium

A multi-gradient microfluidic chip-based neutrophil chemotaxis analysis for sepsis auxiliary diagnosis and prognostic monitoring.

Biosensors & bioelectronics Mar 4, 2026

Sepsis involves life-threatening immune dysregulation where impaired neutrophil chemotaxis is a critical indicator. We developed a multi-gradient neutrophil chemotaxis analysis microfluidic chip (NCA chip) capable of simultaneously generating triple-... read more

Deep learning ultrasonic computed tomography for non-destructive testing of workpieces.

Ultrasonics Mar 4, 2026

Traditional ultrasonic non-destructive testing (NDT) techniques face dual challenges in industrial applications: imaging accuracy and imaging speed. The application of ultrasonic computed tomography (USCT) in the medical field provides a new approach... read more

An AI-assisted framework for the ethical use of machine learning in healthcare.

International journal of medical informatics Mar 4, 2026

This study develops and evaluates ETHICS, a concise, clinician-facing ethical protocol for the routine use of machine learning (ML) in healthcare. Using ChatGPT for first-stage drafting, we generated six actionable principles - Equity and Fairness, T... read more

Online detection of apple moldy core using near-infrared spectroscopy with flexible transmission tray and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 4, 2026

Apple moldy core (AMC) causes substantial postharvest losses, yet early-stage infections remain difficult to detect due to the absence of visible symptoms. This study proposed an integrated, industry-ready approach that combines transmission near-inf... read more

Dual-channel self-supervised multi-task learning for spectral detection of soluble solids content and firmness in Korla fragrant pears.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 4, 2026

To reduce the cost of labeled data in fruit quality detection, this study proposes a deep learning framework combining multi-source spectral data, self-supervised learning (SSL), and multi-task learning (MTL). In this study, spectral data in the visi... read more

An advanced diagnostic framework for discriminating lung cancer tissue subtypes via the synergy of fourier transform infrared spectroscopy and random forest.

Talanta Mar 4, 2026

Accurate subtyping of lung cancer is essential for improving patient prognosis and enabling personalized treatment. However, current clinical techniques are often time-consuming and heavily dependent on the operator's subjective judgment and experien... read more

Twin cross contrastive learning with multi-modality fusion for drug-target affinity prediction.

Artificial intelligence in medicine Mar 4, 2026

Accurate prediction of drug-target binding affinity (DTA) can provide valuable insights for accelerating drug discovery and repositioning. While deep learning has demonstrated remarkable progress in facilitating DTA prediction, most existing methods ... read more

Early electrocardiographic repolarization changes are associated with subclinical cancer therapy-related cardiac dysfunction in lymphoma patients: A machine learning-assisted longitudinal study.

Journal of electrocardiology Mar 4, 2026

UNLABELLED: Anthracycline-induced cardiotoxicity remains a significant clinical challenge. We evaluated longitudinal electrocardiographic (ECG) repolarization changes in 36 lymphoma patients receiving doxorubicin-based chemotherapy and explored their... read more

Decomposition based curriculum-style self-training for source-free universal domain adaptation in computational pathology.

Neural networks : the official journal of the International Neural Network Society Mar 4, 2026

Computational pathology models serve as crucial tools for clinical tasks such as tissue typing, alleviating the burden of manual screening of whole slide images. The stringent ethical regulations on source data, along with agnostic covariate and labe... read more

Dual-mechanism adaptive control for finite/fixed-time synchronization of fuzzy inertial neural networks under parameter uncertainty.