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

Front Oncol. 2024 Dec 2:14:1423405. doi: 10.3389/fonc.2024.1423405. eCollection 2024.

Abstract

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.

Keywords: Dysplasia; Esophageal Cancer; Hyperspectral imaging; Narrow-band imaging; SSD; YOLOv5.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research received support from the National Science and Technology Council, Republic of China through the following grants: NSTC 109-2314-B-037-033, 110-2314-B-037-099, 113-2221-E-037-005-MY2, and 113-2221-E-194-011-MY3. Additionally, financial support was provided by the Kaohsiung Medical University Hospital research project (KMUH111-1M01, SI11105, SI11203, and KMUH-DK(C)113001), as well as the Kaohsiung Medical University Research Center Grant for the Center for Liquid Biopsy and Cohort Research (KMU-TC112B04) in Taiwan.