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Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning

التفاصيل البيبلوغرافية
العنوان: Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning
المؤلفون: Masaru Matsumoto, Mikihiko Karube, Gojiro Nakagami, Aya Kitamura, Nao Tamai, Yuka Miura, Atsuo Kawamoto, Masakazu Kurita, Tomomi Miyake, Chieko Hayashi, Akiko Kawasaki, Hiromi Sanada
المصدر: Applied Sciences, Vol 11, Iss 17, p 7817 (2021)
بيانات النشر: MDPI AG, 2021.
سنة النشر: 2021
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: ultrasonography, pressure injury, automatic classification system, deep learning, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: The classification of ultrasound (US) findings of pressure injury is important to select the appropriate treatment and care based on the state of the deep tissue, but it depends on the operator’s skill in image interpretation. Therefore, US for pressure injury is a procedure that can only be performed by a limited number of highly trained medical professionals. This study aimed to develop an automatic US image classification system for pressure injury based on deep learning that can be used by non-specialists who do not have a high skill in image interpretation. A total 787 training data were collected at two hospitals in Japan. The US images of pressure injuries were assessed using the deep learning-based classification tool according to the following visual evidence: unclear layer structure, cobblestone-like pattern, cloud-like pattern, and anechoic pattern. Thereafter, accuracy was assessed using two parameters: detection performance, and the value of the intersection over union (IoU) and DICE score. A total of 73 images were analyzed as test data. Of all 73 images with an unclear layer structure, 7 showed a cobblestone-like pattern, 14 showed a cloud-like pattern, and 15 showed an anechoic area. All four US findings showed a detection performance of 71.4–100%, with a mean value of 0.38–0.80 for IoU and 0.51–0.89 for the DICE score. The results show that US findings and deep learning-based classification can be used to detect deep tissue pressure injuries.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
53563999
Relation: https://www.mdpi.com/2076-3417/11/17/7817; https://doaj.org/toc/2076-3417
DOI: 10.3390/app11177817
URL الوصول: https://doaj.org/article/a3b06bc6a6aa43689bc53563999e4bc5
رقم الأكسشن: edsdoj.3b06bc6a6aa43689bc53563999e4bc5
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
53563999
DOI:10.3390/app11177817