AIMC Journal:
Machine learning. Health

Background: Substance use disorder is a pressing US public health crisis, with 48.7 million people reporting past-year substance use and over 100 000 overdose deaths in 2022. Building on a growing body of machine learning research using the SAMHSA tr...

In principle, deep learning models trained on medical time-series, including wearable photoplethysmography sensor data, can provide a means to continuously monitor physiological parameters outside of clinical settings. However, there is considerable ...

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiac disorder associated with sudden cardiac death, yet its diagnosis remains slow, resource-intensive, and dependent on expert interpretation of multimodal tests. Machine learn...

Respiratory motion is a long-standing challenge for lung stereotactic body radiotherapy (SBRT), particularly for centrally located lung tumors where increased toxicity demands more precise motion management during treatment. Current two-dimensional i...

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning models captured facial, acoustic, linguistic, and cardio...

Promptable video object segmentation and tracking (VOST) has seen significant advances with the emergence of foundation models like Segment Anything Model 2 (SAM2); however, their application in surgical video analysis remains challenging due to comp...

Accurate delineation of treatment targets and organs at risk (OARs) is essential to the success of radiotherapy (RT). Although artificial intelligence (AI)-based segmentation methods have successfully automated the delineation process, a reliable and...