Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0850
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-22T06:04:03.865367Z
status: published
lens: g_space
sector: health
headline: An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy
dek: 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…
gain_title: 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).
problem_title: (none)
trace_subject: (none)
gain_reading: 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).
gain_evidence: (none)
problem_reading: (none)
problem_evidence: (none)
quick_read: 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.
limitation: 
tag: Evidence-backed gain
key_points: 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.
rundown: 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.
sources:
- peer_reviewed | European Archives of Oto-Rhino-Laryngology | https://doi.org/10.1007/s00405-026-10540-1 | 2026-08-20
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

3bba2e7366bbec92604c617b312c7f1fd7c87e40cb2dc70c83ffeb8b9bb144d6

previous

0000000000000000000000000000000000000000000000000000000000000000