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Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network.
- Source :
-
International journal of pediatric otorhinolaryngology [Int J Pediatr Otorhinolaryngol] 2022 May; Vol. 156, pp. 111096. Date of Electronic Publication: 2022 Mar 18. - Publication Year :
- 2022
-
Abstract
- Objectives: Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN).<br />Study Design: Retrospective, cross-sectional study of medical images. Computer aided design and 3D printed manufacturing.<br />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.<br />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).<br />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.<br /> (Copyright © 2022 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1872-8464
- Volume :
- 156
- Database :
- MEDLINE
- Journal :
- International journal of pediatric otorhinolaryngology
- Publication Type :
- Academic Journal
- Accession number :
- 35334238
- Full Text :
- https://doi.org/10.1016/j.ijporl.2022.111096