Aim: The present study aimed to analyze the use of machine learning in ultrasound (US)-based fecal retention assessment. Methods: 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. Results: 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). Conclusions: 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.
All Science Journal Classification (ASJC) codes
- Research and Theory