Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound

Chih Jui Yu, Hsing Jung Yeh, Chun Chao Chang, Jui Hsiang Tang, Wei Yu Kao, Wen Chao Chen, Yi Jin Huang, Chien Hung Li, Wei Hao Chang, Yun Ting Lin, Herdiantri Sufriyana, Emily Chia Yu Su

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Background and objective: Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misdiagnoses with serious impacts. This study aimed to develop a machine learning system to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses. Methods: Abdominal US images (> 89,000) were collected from 2386 patients in a hospital database. We constructed training sets for gallstones with or without cholecystitis (N = 10,971) and cholecystitis with or without gallstones (N = 7348) as positives. Validation sets were also constructed for gallstones (N = 2664) and cholecystitis (N = 1919). We applied a single-shot multibox detector (SSD) and a feature pyramid network (FPN) to classify and localize objects using image features extracted by ResNet-50 for gallstones, and MobileNet V2 to classify cholecystitis. The deep learning models were pretrained using the COCO-2017 and ILSVRC-2012 datasets. Results: Using the validation sets, the SSD-FPN-ResNet-50 and MobileNet V2 achieved areas under the receiver operating characteristics curve of 0.92 and 0.94, respectively. The inference speeds were 21 (47.6 frames per second, fps) and 7 ms (142.9 fps). Conclusions: A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. This is the first study to develop this system for either gallstone or cholecystitis detection with absence or presence of each one. After clinical trials, this system may be used to assist EPs, including those in remote areas, for detecting these diseases.

Original languageEnglish
Article number106382
JournalComputer Methods and Programs in Biomedicine
Publication statusPublished - Nov 2021


  • Abdomen
  • Computer-aided diagnosis
  • Machine learning
  • Neural network
  • Pattern recognition
  • Ultrasound

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics


Dive into the research topics of 'Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound'. Together they form a unique fingerprint.

Cite this