Matsumoto Masaru, Tsutaoka Takuya, Nakagami Gojiro, Tanaka Shiho, Yoshida Mikako, Miura Yuka, Sugama Junko, Okada Shingo, Ohta Hideki, Sanada Hiromi
Department of Imaging Nursing Science, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Jpn J Nurs Sci. 2020 Oct;17(4):e12340. doi: 10.1111/jjns.12340. Epub 2020 May 11.
The present study aimed to analyze the use of machine learning in ultrasound (US)-based fecal retention assessment.
The accuracy of deep learning techniques and conventional US methods for the evaluation of fecal properties was compared. The presence or absence of rectal feces was analyzed in 42 patients. Eleven patients without rectal fecal retention on US images were excluded from the analysis; thus, fecal properties were analyzed in 31 patients. Deep learning was used to classify the transverse US images into three types: absence of feces, hyperechoic area, and strong hyperechoic area in the rectum.
Of the 42 patients, 31 tested positive for the presence of rectal feces, zero were false positive, zero were false negative, and 11 were negative, indicating a sensitivity of 100% and a specificity of 100% for the detection of rectal feces in the rectum. Of the 31 positive patients, 14 had hard stools and 17 had other types. Hard stool was detected by US findings in 100% of the patients (14/14), whereas deep learning-based classification detected hard stool in 85.7% of the patients (12/14). Other stool types were detected by US findings in 88.2% of the patients (15/17), while deep learning-based classification also detected other stool types in 88.2% of the patients (15/17).
The results showed that US findings and deep learning-based classification can detect rectal fecal retention in older adult patients and distinguish between the types of fecal retention.
本研究旨在分析机器学习在基于超声（US）的粪便潴留评估中的应用。
比较深度学习技术和传统超声方法评估粪便特性的准确性。对42例患者进行直肠粪便存在与否的分析。11例超声图像上无直肠粪便潴留的患者被排除在分析之外；因此，对31例患者的粪便特性进行了分析。使用深度学习将直肠横断超声图像分为三种类型：直肠内无粪便、高回声区和强高回声区。
42例患者中，31例直肠粪便检测呈阳性，假阳性为零，假阴性为零，11例为阴性，表明直肠内直肠粪便检测的灵敏度为100%，特异度为100%。31例阳性患者中，14例为硬便，17例为其他类型。超声检查发现100%的患者（14/14）有硬便，而基于深度学习的分类检测到85.7%的患者（12/14）有硬便。超声检查发现88.2%的患者（15/17）有其他类型的粪便，基于深度学习的分类也检测到88.2%的患者（15/17）有其他类型的粪便。
结果表明，超声检查结果和基于深度学习的分类可以检测老年患者的直肠粪便潴留，并区分粪便潴留的类型。
