Weng Wei-Chun, Huang Chien-Wei, Su Chang-Chao, Mukundan Arvind, Karmakar Riya, Chen Tsung-Hsien, Avhad Amey Rajesh, Chou Chu-Kuang, Wang Hsiang-Chen
Department of Gastroenterology, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st. Rd., Lingya District, Kaohsiung City 80284, Taiwan.
Department of Nursing, Tajen University, 20, Weixin Rd., Yanpu Township, Pingtung County 90741, Taiwan.
Diagnostics (Basel). 2025 Jul 2;15(13):1686. doi: 10.3390/diagnostics15131686.
: Esophageal cancer (EC) is difficult to visually identify, rendering early detection crucial to avert the advancement and decline of the patient's health. : This work aimed to acquire spectral information from EC images via Spectrum-Aided Visual Enhancer (SAVE) technology, which improves imaging beyond the limitations of conventional White-Light Imaging (WLI). The hyperspectral data acquired using SAVE were examined utilizing sophisticated deep learning methodologies, incorporating models such as YOLOv8, YOLOv7, YOLOv6, YOLOv5, Scaled YOLOv4, and YOLOv3. The models were assessed to create a reliable detection framework for accurately identifying the stage and location of malignant lesions. : The comparative examination of these models demonstrated that the SAVE method regularly surpassed WLI for specificity, sensitivity, and overall diagnostic efficacy. Significantly, SAVE improved precision and F1 scores for the majority of the models, which are essential measures for enhancing patient care and customizing effective medicines. Among the evaluated models, YOLOv8 showed exceptional performance. YOLOv8 demonstrated increased sensitivity to squamous cell carcinomas (SCCs), but YOLOv5 provided reliable outcomes across many situations, underscoring its adaptability. : These findings highlight the clinical importance of combining SAVE technology with deep learning models for esophageal cancer screening. The enhanced diagnostic accuracy provided by SAVE, especially when integrated with CAD models, offers potential for improving early detection, precise diagnosis, and tailored treatment approaches in clinically pertinent scenarios.
食管癌（EC）很难通过肉眼识别，因此早期检测对于避免患者健康状况的恶化至关重要。这项工作旨在通过光谱辅助视觉增强器（SAVE）技术从食管癌图像中获取光谱信息，该技术突破了传统白光成像（WLI）的局限性，提升了成像效果。利用复杂的深度学习方法对使用SAVE获取的高光谱数据进行了检验，这些方法包括YOLOv8、YOLOv7、YOLOv6、YOLOv5、Scaled YOLOv4和YOLOv3等模型。对这些模型进行评估，以创建一个可靠的检测框架，用于准确识别恶性病变的阶段和位置。对这些模型的对比检验表明，SAVE方法在特异性、敏感性和总体诊断效能方面通常优于WLI。值得注意的是，SAVE提高了大多数模型的精度和F1分数，这些都是改善患者护理和定制有效药物的关键指标。在评估的模型中，YOLOv8表现出色。YOLOv8对鳞状细胞癌（SCC）的敏感性有所提高，但YOLOv5在多种情况下都能提供可靠的结果，凸显了其适应性。这些发现突出了将SAVE技术与深度学习模型相结合用于食管癌筛查的临床重要性。SAVE提供的更高诊断准确性，尤其是与CAD模型结合时，在临床相关场景中具有改善早期检测、精确诊断和个性化治疗方法的潜力。
