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Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images

Authors
 Gi Nam Kim  ;  Ho Yeol Zhang  ;  Yong Eun Cho  ;  Seung Jun Ryu 
Citation
 HEALTHCARE, Vol.10(6) : 1094, 2022-06 
Journal Title
HEALTHCARE
Issue Date
2022-06
Keywords
bag of visual words ; infrared thermography ; lumbosacral radiculopathy ; machine learning
Abstract
Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.
Files in This Item:
T202205406.pdf Download
DOI
10.3390/healthcare10061094
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
Yonsei Authors
Cho, Yong Eun(조용은) ORCID logo https://orcid.org/0000-0001-9815-2720
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/191556
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