Articlenpj biomedical innovations2026
Machine learning-guided bioelectrical impedance mapping for rapid adjunctive margin assessment in Mohs surgery.
Article in npj biomedical innovations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A label-free high-frequency bioelectrical impedance spectroscopy method, coupled with supervised machine learning, was evaluated as an adjunct to histopathology by generating probability heat maps of excised dermal specimens to estimate the risk of basal cell carcinoma at the surgical margin during Mohs micrographic surgery. In an IRB-approved study of 98 specimens from 55 patients with nodular BCC, a two-step cascade machine-learning algorithm identified cancer-positive tissue locations (ROC AUC 0.878 ± 0.048). To account for tumor-boundary label noise from manual histology misregistration, spatial-tolerance scoring yielded an ROC AUC of 0.993 ± 0.003, with sensitivity of 95.6% ± 4.7% and specificity of 96.1% ± 5.0%. Clinical relevance was addressed by recognizing that, with a median acquisition time of 3.7 min (IQR 2.6-5.5) per specimen, this technology is a non-destructive mapping tool that is easily integrated into the workflow to provide early surgical guidance while preserving frozen sections as the definitive clearance assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.