Introduction
AI diagnosis is used in several medical fields; however, machine learning using deep learning methods is particularly effective in diagnostic imaging and shows high diagnostic capability even in the field of endoscopy. In Japan, endoscopes manufactured by Companies A and B are generally used, and there is a possibility that differences in the image quality will affect AI diagnosis. However, there is no report of studies comparing the AI diagnostic capabilities on images taken by endoscopes of two companies.
Aims & Methods
In endoscopic AI diagnosis of early gastric cancer, the AI diagnostic capabilities were compared using images obtained by endoscopes of Companies A and B for the purpose of clarifying whether there is a need to consider the differences between the endoscopes used. For 30 lesions of 27 patients who underwent endoscopic submucosal dissection for early gastric cancer from April 2019 to March 2022 at our hospital, 356 and 338 endoscopic images of Companies A and B were the target of analysis, respectively.The images were white light (WLI), chromoendoscopy (indigo carmine) and image-enhanced endoscopy, Company A used narrow band imaging (NBI), and Company B used blue laser imaging (BLI).The images were analyzed by AI Medical Systems Inc. using an AI diagnostic system constructed by having UNET learn images taken by either Company A or B's endoscopes as training images. The diagnostic capability was assessed by comparing the specialist-marked correct range of early gastric cancer with an AI-diagnosed range of early gastric cancer, and using a DiceScore of 1.0 for a complete match and 0.0 for no match based on the area of the match.
Results
There was no significant difference in the DiceScore between the endoscopes of both companies in WLI; however, in indigo, Company B scored significantly higher than Company A. There was no significant difference between NBI and BLI. A stratification analysis by macroscopic examination of the lesions showed that in 0-IIc, Company A scored significantly higher in WLI than Company B, while in indigo, Company B scored significantly higher than Company A. In 0-IIa+IIc, Company B scored significantly higher than Company A in WLI; however, there was no difference between the two companies in indigo. In comparison by macroscopic examination of the lesions, 0-IIa+IIc lesions scored significantly higher than 0-IIc lesions in indigo for Company A, and in WLI for Company B. When the lesions were divided into a maximum diameter of <2 cm, 2–3 cm, and ≥3 cm, lesions ≥3 cm scored lower than lesions in the other groups.
Conclusion
Endoscopic images of Company B tended to have higher sharpness and stronger bluish color compared to endoscopic images of Company A. This might be the reason why the diagnostic capability improved after indigo-carmine scattering. Therefore, proactive scattering of indigo carmine was considered preferable when observing with the Company B endoscope. Presumably, the reason 0-IIa+IIc lesions had higher diagnostic performance than 0-IIc lesions was due to differences in the elevation of the lesions. The Company A endoscope was considered significantly superior for 0-IIc lesions, which are easily overlooked in clinical practice. The complexity of the morphology of the lesion increases in large lesions, which widens the gap between the AI diagnostic range and correct range. There are lesions wherein the AI diagnostic performance increased with both endoscopes of Company A and that of Company B. Therefore, it is preferable to use the endoscopes in AI diagnosis taking these facts into consideration.
References
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