Publication Date
12-14-2023
Journal
Scientific Reports
DOI
10.1038/s41598-023-49197-9
PMID
38097594
PMCID
PMC10721617
PubMedCentral® Posted Date
12-14-2023
PubMedCentral® Full Text Version
Post-Print
Published Open-Access
yes
Keywords
Humans, United States, Deep Learning, Endoscopy, Diagnostic Imaging, Anus Neoplasms, HIV Infections, Cancer imaging, Cancer prevention, Anal cancer, Diagnostic markers
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.
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Digestive System Diseases Commons, Gastroenterology Commons, Medical Sciences Commons, Oncology Commons, Radiology Commons
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