PDF(Pubmed)
Abstract：
OBJECTIVE: Poorly visualized images that appear during small bowel capsule endoscopy (SBCE) can confuse the interpretation of small bowel lesions and increase the physician\'s workload. Using a validated artificial intelligence (AI) algorithm that can evaluate the mucosal visualization, we aimed to assess whether SBCE reading after the removal of poorly visualized images could affect the diagnosis of SBCE.
METHODS: A study was conducted to analyze 90 SBCE cases in which a small bowel examination was completed. Two experienced endoscopists alternately performed two types of readings. They used the AI algorithm to remove poorly visualized images for the frame reduction reading (AI user group) and conducted whole frame reading without AI (AI non-user group) for the same patient. A poorly visualized image was defined as an image with < 50% mucosal visualization. The study outcomes were diagnostic concordance and reading time between the two groups. The SBCE diagnosis was classified as Crohn\'s disease, bleeding, polyp, angiodysplasia, and nonspecific finding.
RESULTS: The final SBCE diagnoses between the two groups showed statistically significant diagnostic concordance (k = 0.954, p < 0.001). The mean number of lesion images was 3008.5 ± 9964.9 in the AI non-user group and 1401.7 ± 4811.3 in the AI user group. There were no cases in which lesions were completely removed. Compared with the AI non-user group (120.9 min), the reading time was reduced by 35.6% in the AI user group (77.9 min).
CONCLUSIONS: SBCE reading after reducing poorly visualized frames using the AI algorithm did not have a negative effect on the final diagnosis. SBCE reading method integrated with frame reduction and mucosal visualization evaluation will help improve AI-assisted SBCE interpretation.
METHODS: A study was conducted to analyze 90 SBCE cases in which a small bowel examination was completed. Two experienced endoscopists alternately performed two types of readings. They used the AI algorithm to remove poorly visualized images for the frame reduction reading (AI user group) and conducted whole frame reading without AI (AI non-user group) for the same patient. A poorly visualized image was defined as an image with < 50% mucosal visualization. The study outcomes were diagnostic concordance and reading time between the two groups. The SBCE diagnosis was classified as Crohn\'s disease, bleeding, polyp, angiodysplasia, and nonspecific finding.
RESULTS: The final SBCE diagnoses between the two groups showed statistically significant diagnostic concordance (k = 0.954, p < 0.001). The mean number of lesion images was 3008.5 ± 9964.9 in the AI non-user group and 1401.7 ± 4811.3 in the AI user group. There were no cases in which lesions were completely removed. Compared with the AI non-user group (120.9 min), the reading time was reduced by 35.6% in the AI user group (77.9 min).
CONCLUSIONS: SBCE reading after reducing poorly visualized frames using the AI algorithm did not have a negative effect on the final diagnosis. SBCE reading method integrated with frame reduction and mucosal visualization evaluation will help improve AI-assisted SBCE interpretation.
摘要：
目的：小肠胶囊内窥镜检查（SBCE）过程中出现的不良可视化图像可能会混淆小肠病变的解释并增加医生的工作量。使用经过验证的人工智能（AI）算法，可以评估粘膜可视化，我们的目的是评估在去除不良可视化图像后的SBCE读数是否会影响SBCE的诊断.
方法：对90例完成小肠检查的SBCE病例进行分析。两位经验丰富的内窥镜医师交替进行两种类型的读数。他们使用AI算法为帧减少阅读（AI用户组）删除了可视化不佳的图像，并为同一患者进行了无AI（AI非用户组）的整帧阅读。不良可视化图像被定义为粘膜可视化<50%的图像。研究结果是两组之间的诊断一致性和阅读时间。SBCE诊断被归类为克罗恩病，出血,息肉,血管发育不良,和非特异性发现。
结果：两组间的最终SBCE诊断显示出统计学上显著的诊断一致性（k=0.954，p<0.001）。AI非用户组的平均病变图像数量为3008.5±9964.9，AI用户组的平均病变图像数量为1401.7±4811.3。没有病变完全切除的病例。与AI非用户组（120.9分钟）相比，AI用户组的阅读时间减少了35.6%(77.9分钟).
结论：使用AI算法减少可视化较差的帧后的SBCE读数对最终诊断没有负面影响。SBCE阅读方法结合减框和粘膜可视化评估将有助于改善AI辅助的SBCE解释。
方法：对90例完成小肠检查的SBCE病例进行分析。两位经验丰富的内窥镜医师交替进行两种类型的读数。他们使用AI算法为帧减少阅读（AI用户组）删除了可视化不佳的图像，并为同一患者进行了无AI（AI非用户组）的整帧阅读。不良可视化图像被定义为粘膜可视化<50%的图像。研究结果是两组之间的诊断一致性和阅读时间。SBCE诊断被归类为克罗恩病，出血,息肉,血管发育不良,和非特异性发现。
结果：两组间的最终SBCE诊断显示出统计学上显著的诊断一致性（k=0.954，p<0.001）。AI非用户组的平均病变图像数量为3008.5±9964.9，AI用户组的平均病变图像数量为1401.7±4811.3。没有病变完全切除的病例。与AI非用户组（120.9分钟）相比，AI用户组的阅读时间减少了35.6%(77.9分钟).
结论：使用AI算法减少可视化较差的帧后的SBCE读数对最终诊断没有负面影响。SBCE阅读方法结合减框和粘膜可视化评估将有助于改善AI辅助的SBCE解释。
