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  4. Automatic Detection of Non-Diagnostic Pediatric Lateral Airway/Soft Tissue Neck Radiographic Exams Using Deep Learning at the Point of Care

Automatic Detection of Non-Diagnostic Pediatric Lateral Airway/Soft Tissue Neck Radiographic Exams Using Deep Learning at the Point of Care

E Somasundaram1*, J Dillman2 , E Crotty3 , B Coley4 , S Brady5 , (1) Cincinnati Children's Hospital Medical Center, Cincinnati, OH, (2) Cincinnati Children's Hospital, Cincinnati, OH, (3) ,Cincinnati, ,(4) Cincinnati Children's Hospital and Medical Center, Cincinnati, ,(5) Cincinnati Childrens Hospital Med Ctr, Cincinnati, OH

Presentations

(Thursday, 7/18/2019) 1:00 PM - 3:00 PM

Room: 302

Purpose: Development and validation of a deep learning network to automatically detect non-diagnostic digital radiograph (DR) lateral airway/soft tissue neck examinations, at the point of care, that should be repeated before the technologist completes the examination.

Methods: Two radiologists independently reviewed 1121 pediatric lateral airway/soft tissue neck radiographs and classified them into cases that required a repeat examination (bad scans, due to poor visualization of the airway and/or relevant soft tissues) and that were diagnostic (good scans). When the two radiologists did not come to a consensus, a third radiologist reviewed the image and the majority consensus was used for the final classification. The review process resulted in 682 no-repeat cases and 439 repeat cases. A deep learning model based on the GoogleNet convolutional neural network architecture with inception modules was used to train the classifier. The training set consisted of 366 no-repeat cases and 249 repeat cases. Data augmentation was used both at the training and validation phases to increase the training images and reduce over-fitting. The Keras framework with Tensorflow-backend was used for implementation. Diagnostic performance of the final model was evaluated using sensitivity and specificity of the model to detect non-diagnostic scans.

Results: Discriminating between airway examinations requiring a repeat scan achieved sensitivity, specificity and accuracy values of 0.85, 0.83 and 0.83, respectively when tested on a dataset of 514 images (421 no-repeat and 92 repeat cases) with a median age of 4 ± 9 years and male to female ratio of 0.56.

Conclusion: A deep learning model that automatically detects non-diagnostic pediatric DR airway X-ray examinations has been developed and validated. The initial results show potential for a point-of-care model that can flag bad radiographic images that require repeating immediately without radiologist intervention.

Keywords

Quality Control, X Rays, Computer Vision

Taxonomy

IM- X-ray: Quality Control

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