Predicting prolonged postoperative ileus in gastric cancer patients based on bowel sounds using intelligent auscultation and machine learning

Figure 1 Patient selection process.

Figure 2 Flowchart of bowel sound collection in clinical department. A registered wireless bowel sound collector was connected to the system and placed in the patient's right lower abdomen (ileocecal region). Bowel sounds were collected through the local area network. Bowel sound collector is indicted in blue, and the computer represents the terminal.

Figure 3 Bowel rate on first postoperative day. The yellow area represents the area of hyperactive bowel sounds, green represents the area of normal bowel sounds, and blue represents the area of diminished bowel sounds. BR: Bowel rate.

Figure 4 Bowel sound data analysis. Bowel sound vibration amplitude presentation form, representing the intensity of the bowel sound in the time or frequency domain. BSVA: Bowel sound vibration amplitude.

Figure 5 Bowel sound analysis. Data frequency of bowel sounds presentation form, i.e., significant frequency components of bowel sounds, frequency bands of energy concentration in the frequency domain of bowel sounds.

Figure 6 Comparison of area under the receiver operating characteristic curves between eight machine learning models in the training cohort. A: Performance of eight models in the training cohort; B: Performance of eight models in the internal validation cohort. The Brier class represents the Brier score; C: Calibration curves of the eight models in the training; D: Calibration curves of the eight models in the internal validation cohorts; E: Comparison of decision curve analysis (DCA) curves between eight machine learning models in the training cohort; F: Comparison of DCA curves between eight machine learning models in the internal validation cohort.