Academic radiology · 2024
Exploring Deep Learning Applications using Ultrasound Single View Cines in Acute Gallbladder Pathologies: Preliminary Results.
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Ge Connie, Jang Junbong, Svrcek Patrick, Fleming Victoria, Kim Young H
Аннотация
RATIONALE AND OBJECTIVES: In this preliminary study, we aimed to develop a deep learning model using ultrasound single view cines that distinguishes between imaging of normal gallbladder, non-urgent cholelithiasis, and acute calculous cholecystitis requiring urgent intervention.
METHODS: Adult patients presenting to the emergency department between 2017-2022 with right-upper-quadrant pain were screened, and ultrasound single view cines of normal imaging, non-urgent cholelithiasis, and acute cholecystitis were included based on final clinical diagnosis. Longitudinal-view cines were de-identified and gallbladder pathology was annotated for model training. Cines were randomly sorted into training (70%), validation (10%), and testing (20%) sets and divided into 12-frame segments. The deep learning model classified cines as normal (all segments normal), cholelithiasis (normal and non-urgent cholelithiasis segments), and acute cholecystitis (any cholecystitis segment present).
RESULTS: A total of 186 patients with 266 cines were identified: Normal imaging (52 patients; 104 cines), non-urgent cholelithiasis (73;88), and acute cholecystitis (61;74). The model achieved a 91% accuracy for Normal vs. Abnormal imaging and an 82% accuracy for Urgent (acute cholecystitis) vs. Non-urgent (cholelithiasis or normal imaging). Furthermore, the model identified abnormal from normal imaging with 100% specificity, with no false positive results.
CONCLUSION: Our deep learning model, using only readily obtained single-view cines, exhibited a high degree of accuracy and specificity in discriminating between non-urgent imaging and acute cholecystitis requiring urgent intervention.
METHODS: Adult patients presenting to the emergency department between 2017-2022 with right-upper-quadrant pain were screened, and ultrasound single view cines of normal imaging, non-urgent cholelithiasis, and acute cholecystitis were included based on final clinical diagnosis. Longitudinal-view cines were de-identified and gallbladder pathology was annotated for model training. Cines were randomly sorted into training (70%), validation (10%), and testing (20%) sets and divided into 12-frame segments. The deep learning model classified cines as normal (all segments normal), cholelithiasis (normal and non-urgent cholelithiasis segments), and acute cholecystitis (any cholecystitis segment present).
RESULTS: A total of 186 patients with 266 cines were identified: Normal imaging (52 patients; 104 cines), non-urgent cholelithiasis (73;88), and acute cholecystitis (61;74). The model achieved a 91% accuracy for Normal vs. Abnormal imaging and an 82% accuracy for Urgent (acute cholecystitis) vs. Non-urgent (cholelithiasis or normal imaging). Furthermore, the model identified abnormal from normal imaging with 100% specificity, with no false positive results.
CONCLUSION: Our deep learning model, using only readily obtained single-view cines, exhibited a high degree of accuracy and specificity in discriminating between non-urgent imaging and acute cholecystitis requiring urgent intervention.