AIMC Topic: Adult

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Identification of clinically meaningful, overlapping obstructive respiratory disease subtypes via data-driven approaches in a primary care population.

BMC pulmonary medicine
BACKGROUND: Obstructive respiratory conditions, including asthma, bronchiectasis, and chronic obstructive pulmonary disease (COPD), are increasingly recognised as heterogeneous syndromes with significant overlap. Multiple disease pathways contribute ...

Characterizing immune profiles in hepatocellular carcinoma patients benefiting from pembrolizumab and lenvatinib using machine learning.

BMC cancer
BACKGROUND: Combination immunotherapies, such as pembrolizumab plus lenvatinib (PL), are commonly used in treatment for unresectable hepatocellular carcinoma (uHCC). However, it remains challenging to predict which patients will benefit from this the...

Prediction of advanced chronic kidney disease through retinal fundus images by deep learning.

Scientific reports
This study was developed and evaluated deep learning model for detecting chronic kidney disease (CKD) by retinal fundus images. This study included 42,963 clinical visits from 17,442 patients who underwent retinal fundus examination between October 1...

Artificial intelligence for predicting depression anxiety and stress using psychometric data.

Scientific reports
Mental health is a crucial aspect of overall well-being, yet it is often overlooked due to stigma and limited accessibility to care. This study investigates the ability of artificial intelligence (AI) to predict common psychological conditions, depre...

Reducing annotation burden in physical activity research using vision language models.

Scientific reports
Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machin...

Machine learning for early prediction of secondary cancer after radiotherapy.

Scientific reports
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...

Modality-projection universal model for comprehensive full-body medical imaging segmentation.

Nature communications
The integration of deep learning in medical imaging has significantly advanced diagnostic, therapeutic, and research outcomes. However, applying universal models across multiple modalities remains challenging due to inherent inter-modality variabilit...

Application of AI Communication Training Tools in Medical Undergraduate Education: Mixed Methods Feasibility Study Within a Primary Care Context.

JMIR medical education
BACKGROUND: Effective communication is fundamental to high-quality health care delivery, influencing patient satisfaction, adherence to treatment plans, and clinical outcomes. However, communication skills training for medical undergraduates often fa...

Evaluation of radiosensitivity for high grade gliomas patients using a multi-temporal graph convolutional networks.

Physics in medicine and biology
Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple ti...

MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol.

PloS one
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to convention...