Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Oct 08, 2021)
Date Submitted: Sep 8, 2021
Open Peer Review Period: Sep 6, 2021 - Oct 8, 2021
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Development and validation of a deep learning system for comprehensive imaging quality check to classify body parts and contrast enhancement
ABSTRACT
Background:
Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, image quality check is still performed manually by image analysts, which requires a lot of manpower and time.
Objective:
This study aimed to develop a deep learning model that simultaneously identifies anatomical locations and contrast enhancement on medical images, with accuracy and clinical effectiveness validation, to support an automated image quality check.
Methods:
In this retrospective study, 1,669 computed tomography (CT) images with five specific anatomical locations were collected from Asan Medical Center and Kangdong Sacred Heart Hospital. To generate the ground truth, two radiologists reviewed the anatomical locations and presence of contrast enhancement using the collected data. A deep learning framework called ImageQC-net (Image Quality Check-network) with transfer learning was developed using an InceptioResNetV2 model. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared.
Results:
The ImageQC-net body part classification showed an excellent performance in both internal (precision, 100%; recall, 100%; and accuracy, 100%) and external validation sets (precision, 99.34%; recall, 99.33%; and accuracy, 99.33%). In addition, the contrast-enhanced classification performance achieved 100% precision, recall, and accuracy in the internal validation set and ~100% accuracy in the external dataset (precision, 99.76%; recall, 99.79%; and accuracy, 99.78%). When integrating the model that achieved the best performance, the overall accuracy was 99.1%. For clinical effectiveness, the time reduction by artificial intelligence (AI)-aided quality check of both analyst 1 and 2 (49.7% and 48.3% decrease, respectively) was statistically significant (p < 0.001).
Conclusions:
Comprehensive AI techniques to identify body parts and contrast enhancement on CT images are highly accurate and can significantly reduce the time spent on image quality checks.
Citation
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