Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement

• Published in: BMC medical imaging Vol. 22; no. 1; p. 87
• Main Authors: Na, Seongwon, Sung, Yu Sub, Ko, Yousun, Shin, Youngbin, Lee, Junghyun, Ha, Jiyeon, Ham, Su Jung, Yoon, Kyoungro, Kim, Kyung Won
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.05.2022; BioMed Central; BMC
• Subjects: Abdomen; Accuracy; Archives & records; Artificial Intelligence; Body parts; Classification; Clinical trials; Computed tomography; Computed tomography series; CT imaging; Data collection; Datasets; Deep Learning; Diagnostic imaging; Health aspects; Health care facilities; Human Body; Humans; Image contrast; Image enhancement; Image quality; Image quality check; Machine learning; Manpower; Medical centers; Medical imaging; Performance evaluation; Quality control; Recall; Retrospective Studies; Statistical analysis; Tomography, X-Ray Computed - methods; Transfer learning; Two-dimensional convert method; Verification; Deep learning; Two-dimensional convert method; Computed tomography series; Transfer learning; Artificial intelligence; Image quality check
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-022-00815-4

Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the predefined rules, is still performed manually by image analysts, which requires a lot of manpower and time. In this retrospective study, 1669 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. The individual deep learning model is developed through InceptionResNetv2 and transfer learning, and we propose Image Quality Check-Net (Image QC-Net), an ensemble AI model that utilizes it. To evaluate their clinical effectiveness, the overall accuracy and time spent on image quality check of a conventional model and ImageQC-net were compared. ImageQC-net body part classification showed excellent performance in both internal (precision, 100%; recall, 100% accuracy, 100%) and external verification sets (precision, 99.8%; recovery rate, 99.8%, accuracy, 99.8%). In addition, contrast enhancement classification performance achieved 100% precision, recall, and accuracy in the internal verification set and achieved (precision, 100%; recall, 100%; accuracy 100%) in the external dataset. In the case of clinical effects, the reduction of time by checking the quality of artificial intelligence (AI) support by analysts 1 and 2 (49.7% and 48.3%, respectively) was statistically significant (p < 0.001). 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.