Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network. (May 2022)

• Record Type: Journal Article
• Authors: Patel, Palak; Ragland, Katelyn; Robertson, Brianna; Ragusa, Gabriel; Wiley, Christine; Miller, Jacob; Jullens, Robert; Dunham, Michael; Richter, Gresham
• Is Part Of: International journal of pediatric otorhinolaryngology. Volume 156(2022)
• Publication Date: 2022-05
• Subjects: Otolaryngology -- Periodicals; Pediatrics -- Periodicals; Otolaryngology -- Periodicals; Pediatrics -- Periodicals; Oto-rhino-laryngologie -- Périodiques; Pédiatrie -- Périodiques; 618.9209751
• DOI: 10.1016/j.ijporl.2022.111096
• Languages: English
• ISSNs: 0165-5876

Abstract

Abstract: Objectives: Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN). Study design: Retrospective, cross-sectional study of medical images. Computer aided design and 3D printed manufacturing. Methods: We obtained a series of head and neck vascular anomaly images in pediatric patients from the database maintained in a large multidisciplinary vascular anomalies clinic. The database was supplemented with additional images from the internet. Four diagnostic classes were recognized in the dataset – infantile hemangioma, capillary malformation, venous malformation, and arterio-venous malformation. Our group designed and implemented a convolutional neural network to recognize the four classes of vascular anomalies as well as a fifth class consisting of none of the vascular anomalies. The system was based on the Inception-Resnet neural network using transfer learning. For deployment, we designed and built a compact, handheld device including a central processing unit, display subsystems, and control electronics. The device focuses upon and autonomously classifies pediatric vascular lesions. Results: The multiclass system distinguished the diagnostic categories with an overall accuracy of 84%. The inclusion of lesion metadata improved overall accuracy to 94%. Sensitivity ranged from 88% (venous malformation) to 100% (arterio-venous malformation and capillary malformation). Conclusions: An easily deployed handheld device to autonomously diagnose pediatric skin lesions is feasible. Large training datasets and novel neural network architectures will be required for successful implementation.