Frontiers in Oncology (Dec 2024)

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

  • Yao-Kuang Wang,
  • Yao-Kuang Wang,
  • Yao-Kuang Wang,
  • Riya Karmakar,
  • Arvind Mukundan,
  • Ting-Chun Men,
  • Yu-Ming Tsao,
  • Song-Cun Lu,
  • I-Chen Wu,
  • I-Chen Wu,
  • Hsiang-Chen Wang,
  • Hsiang-Chen Wang,
  • Hsiang-Chen Wang

DOI
https://doi.org/10.3389/fonc.2024.1423405
Journal volume & issue
Vol. 14

Abstract

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IntroductionThe 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.MethodsIn 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).ResultsAn 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.ConclusionThe 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.

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