Editorial: Harnessing machine learning for enhanced biomedical diagnosis and early disease detection: bridging data science and healthcare
Mahendra Gawali, Hsiang-Chen Wang, Arvind Mukundan · 2026
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Description
Mahendra Gawali, Hsiang-Chen Wang, and Arvind Mukundan open a Research Topic in a 2026 editorial on machine learning for biomedical diagnosis and disease detection. Cancer caught late frames the collection: algorithms that read scans, signals, and lab panels promise faster, personal detection than unaided review.
The editorial tours contributions spanning medical imaging, wearable data, and biomanufacturing. One group classified endoscopic ultrasound images from 219 patients, pairing deep learning with radiomic features to reach an AUC of 0.95. Another sorted 1,498 diabetic patients by microvascular complication risk, with gradient boosting on top and blood markers such as urea, fibrinogen, and D-dimer among the signals. Lung cancer work on 197 surgical patients combined spectral CT radiomics with deep learning to predict tumor spread through air spaces, logistic regression leading at an AUC of 0.918, while a colon cancer team modelled CT scans from 247 patients. The editors frame the set as data science and healthcare meeting.
Catalog record
| CREATOR | Mahendra Gawali, Hsiang-Chen Wang, Arvind Mukundan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 13943 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |