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Brenes D, Kortum A, Coole J, Carns J, Schwarz R, Vohra I, Richards-Kortum R, Liu Y, Cai Z, Sigel K, Anandasabapathy S, Gaisa M, Chiao E. Deployment and assessment of a deep learning model for real-time detection of anal precancer with high frame rate high-resolution microendoscopy. Sci Rep 2023; 13:22267. [PMID: 38097594 PMCID: PMC10721617 DOI: 10.1038/s41598-023-49197-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
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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Affiliation(s)
- David Brenes
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA.
| | - Alex Kortum
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA
| | - Jackson Coole
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA
| | - Jennifer Carns
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA
| | - Richard Schwarz
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA
| | - Imran Vohra
- Biotex, 114 Holmes Rd., Houston, TX, 77045, USA
| | - Rebecca Richards-Kortum
- Department of Bioengineering, Rice University, MS-142 6100 Main St., Houston, TX, 77005, USA
| | - Yuxin Liu
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1468 Madison Avenue, New York, NY, 10029, USA
| | - Zhenjian Cai
- Clinical Pathology Laboratories, 9200 Wall Street, Austin, TX, 78754, USA
| | - Keith Sigel
- Department of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA
| | | | - Michael Gaisa
- Department of Medicine, Icahn School of Medicine at Mount Sinai, One Gustave Levy Place, New York, NY, 10029, USA
| | - Elizabeth Chiao
- Department of Epidemiology, Division of Cancer Prevention, University of Texas - MD Anderson Cancer Center, 1155 Pressler St., Unit 1340, Houston, TX, 77030, USA.
- Department Epidemiology, Division of Cancer Prevention, and Department of General Oncology, Division of Cancer Medicine, University of Texas - MD Anderson Cancer Center, 1155 Pressler St., Unit 1340, Houston, TX, 77030, USA.
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Tsilivigkos C, Athanasopoulos M, Micco RD, Giotakis A, Mastronikolis NS, Mulita F, Verras GI, Maroulis I, Giotakis E. Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review. J Clin Med 2023; 12:6973. [PMID: 38002588 PMCID: PMC10672270 DOI: 10.3390/jcm12226973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords 'otorhinolaryngology', 'imaging', 'computer vision', 'artificial intelligence', and 'deep learning', and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.
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Affiliation(s)
- Christos Tsilivigkos
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Michail Athanasopoulos
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Riccardo di Micco
- Department of Otolaryngology and Head and Neck Surgery, Medical School of Hannover, 30625 Hannover, Germany;
| | - Aris Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
| | - Nicholas S. Mastronikolis
- Department of Otolaryngology, University Hospital of Patras, 265 04 Patras, Greece; (M.A.); (N.S.M.)
| | - Francesk Mulita
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Georgios-Ioannis Verras
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Ioannis Maroulis
- Department of Surgery, University Hospital of Patras, 265 04 Patras, Greece; (G.-I.V.); (I.M.)
| | - Evangelos Giotakis
- 1st Department of Otolaryngology, National and Kapodistrian University of Athens, Hippocrateion Hospital, 115 27 Athens, Greece; (A.G.); (E.G.)
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