Sheeba J Sujit1, Eliana Bonfante2, Azin Aein3, Ivan Coronado1, Roy Riascos-Castaneda3, Luca Giancardo4. 1. Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, United States. 2. Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, McGovern Medical School, United States. 3. Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, McGovern Medical School, United States; Memorial Hermann Hospital, Texas Medical Center, United States. 4. Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, United States. Electronic address: luca.giancardo@uth.tmc.edu.
Abstract
BACKGROUND AND OBJECTIVE: Accurate information concerning implanted medical devices prior to a Magnetic resonance imaging (MRI) examination is crucial to assure safety of the patient and to address MRI induced unintended changes in device settings. The identification of these devices still remains a very challenging task. In this paper, with the aim of providing a faster device detection, we propose the adoption of deep learning for medical device detection from X-rays. METHOD: In particular, we propose a pipeline for the identification of implanted programmable cerebrospinal fluid shunt valves using X-ray images of the radiologist workstation screens captured with mobile phone integrated cameras at different angles and illuminations. We compare the proposed convolutional neural network with published methods. RESULTS: Experimental results show that this approach outperforms methods trained on images digitally transferred directly from the scanners and then applied on mobile phones images (mean accuracy 95% vs 77%, Avg. Precision 0.96 vs 0.77, Avg. Recall 0.95 vs 0.77, Avg. F1-score 0.95 vs 0.77) and existing published methods based on transfer learning fine-tuned directly on the mobile phone images (mean accuracy 94% vs 75%, Avg. Precision 0.94 vs 0.75, Avg. Recall 0.94 vs 0.75, Avg. F1-score 0.94 vs 0.75). CONCLUSION: An automated shunt valve identification system is a promising safety tool for radiologists to efficiently coordinate the care of patients with implanted devices. An image-based safety system able to be deployed on a mobile phone would have significant advantages over methods requiring direct input from X-ray scanners or clinical picture archiving and communication system (PACS) in terms of ease of integration in the hospital or clinical ecosystems.
BACKGROUND AND OBJECTIVE: Accurate information concerning implanted medical devices prior to a Magnetic resonance imaging (MRI) examination is crucial to assure safety of the patient and to address MRI induced unintended changes in device settings. The identification of these devices still remains a very challenging task. In this paper, with the aim of providing a faster device detection, we propose the adoption of deep learning for medical device detection from X-rays. METHOD: In particular, we propose a pipeline for the identification of implanted programmable cerebrospinal fluid shunt valves using X-ray images of the radiologist workstation screens captured with mobile phone integrated cameras at different angles and illuminations. We compare the proposed convolutional neural network with published methods. RESULTS: Experimental results show that this approach outperforms methods trained on images digitally transferred directly from the scanners and then applied on mobile phones images (mean accuracy 95% vs 77%, Avg. Precision 0.96 vs 0.77, Avg. Recall 0.95 vs 0.77, Avg. F1-score 0.95 vs 0.77) and existing published methods based on transfer learning fine-tuned directly on the mobile phone images (mean accuracy 94% vs 75%, Avg. Precision 0.94 vs 0.75, Avg. Recall 0.94 vs 0.75, Avg. F1-score 0.94 vs 0.75). CONCLUSION: An automated shunt valve identification system is a promising safety tool for radiologists to efficiently coordinate the care of patients with implanted devices. An image-based safety system able to be deployed on a mobile phone would have significant advantages over methods requiring direct input from X-ray scanners or clinical picture archiving and communication system (PACS) in terms of ease of integration in the hospital or clinical ecosystems.
Authors: Andrea Lavinio; Sally Harding; Floor Van Der Boogaard; Marek Czosnyka; Peter Smielewski; Hugh K Richards; John D Pickard; Zofia H Czosnyka Journal: J Neurosurg Pediatr Date: 2008-09 Impact factor: 2.375
Authors: Bob D de Vos; Floris F Berendsen; Max A Viergever; Hessam Sokooti; Marius Staring; Ivana Išgum Journal: Med Image Anal Date: 2018-12-08 Impact factor: 8.545
Authors: Refaat E Gabr; Ivan Coronado; Melvin Robinson; Sheeba J Sujit; Sushmita Datta; Xiaojun Sun; William J Allen; Fred D Lublin; Jerry S Wolinsky; Ponnada A Narayana Journal: Mult Scler Date: 2019-06-13 Impact factor: 6.312