Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network.

Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN). Retrospective, cross-sectional study of medical images. Computer aided design and 3D printed manufacturing. 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. 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). 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.

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