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An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need. However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed. Methods We con…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-0850· cite & verify
An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy
UNIFORM AND MULTI-GRID MODELING OF ACOUSTIC WAVE PROPAGATION WITH CELLULAR AUTOMATON TECHNIQUES by Bailey, Jermaine A.. Public domain

In brief

Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need.

However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed. Methods We conducted a retrospective study on 611 patients who underwent white light endoscopy (WLE) examinations.

Main points

  1. Background Laryngeal cancer is a significant global health issue with high mortality, and early diagnosis is critical for survival.
  2. Developing accurate diagnostic models for laryngoscopy can reduce potential repeated biopsies and lessen the patient burden, representing an urgent clinical need.
  3. However, existing artificial intelligence models often function as black boxes and are trained on single, pre-selected images, which does not reflect the clinical workflow where multiple images are assessed.

The gain

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy: Results IMIL-Net achieved the highest diagnostic performance, with a mean area under the curve (AUC) of 0.975 (95% CI 0.959-0.991), accuracy of 0.915 (95% CI 0.883-0.947), sensitivity of 0.876 (95% CI 0.803-0.949), and specificity of 0.945 (95% CI 0.910-0.980).

Sources

  1. Peer-reviewedEuropean Archives of Oto-Rhino-Laryngology2026-08-20

The debate