Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy

David Brenes, Alex Kortum, Jackson Coole, Jennifer Carns, Richard Schwarz, Imran Vohra, Rebecca Richards-Kortum, Yuxin Liu, Zhenjian Cai, Keith Sigel, Sharmila Anandasabapathy, Michael Gaisa, Elizabeth Chiao

Research output: Contribution to journalArticlepeer-review

Abstract

Anal cancer incidence is significantly higher in people living with HIV as HIV increases the oncogenic potential of human papillomavirus. The incidence of anal cancer in the United States has recently increased, with diagnosis and treatment hampered by high loss-to-follow-up rates. Novel methods for the automated, real-time diagnosis of AIN 2+ could enable "see and treat" strategies, reducing loss-to-follow-up rates. A previous retrospective study demonstrated that the accuracy of a high-resolution microendoscope (HRME) coupled with a deep learning model was comparable to expert clinical impression for diagnosis of AIN 2+ (sensitivity 0.92 [P = 0.68] and specificity 0.60 [P = 0.48]). However, motion artifacts and noise led to many images failing quality control (17%). Here, we present a high frame rate HRME (HF-HRME) with improved image quality, deployed in the clinic alongside a deep learning model and evaluated prospectively for detection of AIN 2+ in real-time. The HF-HRME reduced the fraction of images failing quality control to 4.6% by employing a high frame rate camera that enhances contrast and limits motion artifacts. The HF-HRME outperformed the previous HRME (P < 0.001) and clinical impression (P < 0.0001) in the detection of histopathologically confirmed AIN 2+ with a sensitivity of 0.91 and specificity of 0.87.

Original languageEnglish
Article number22267
JournalScientific Reports
Volume13
Issue number1
DOIs
StatePublished - Dec 2023

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