Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms.

Autor:	Wang YK; Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.; Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.; Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan., Karmakar R; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan., Mukundan A; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan., Men TC; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan., Tsao YM; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan., Lu SC; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan., Wu IC; Division of Gastroenterology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.; Department of Medicine, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan., Wang HC; Department of Mechanical Engineering, National Chung Cheng University, Chiayi, Taiwan.; Department of Medical Research, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan.; Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung, Taiwan.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2024 Dec 02; Vol. 14, pp. 1423405. Date of Electronic Publication: 2024 Dec 02 (Print Publication: 2024).
DOI:	10.3389/fonc.2024.1423405
Abstrakt:	Introduction: The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer. Methods: In this study, we expanded upon traditional red-green-blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC). Results: An additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively. Conclusion: The HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer. Competing Interests: Author H.-C.W was employed by the company Hitspectra Intelligent Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Wang, Karmakar, Mukundan, Men, Tsao, Lu, Wu and Wang.)
Databáze:	MEDLINE
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