Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images
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...
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MDPI AG
2022-06-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/6/1094 |
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| author | Gi Nam Kim Ho Yeol Zhang Yong Eun Cho Seung Jun Ryu |
| author_facet | Gi Nam Kim Ho Yeol Zhang Yong Eun Cho Seung Jun Ryu |
| author_sort | Gi Nam Kim |
| collection | DOAJ |
| description | 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. |
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| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T23:41:02Z |
| publishDate | 2022-06-01 |
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| spelling | doaj.art-8a034426ba7a4be7b74a6050814683d12023-11-23T16:52:48ZengMDPI AGHealthcare2227-90322022-06-01106109410.3390/healthcare10061094Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic ImagesGi Nam Kim0Ho Yeol Zhang1Yong Eun Cho2Seung Jun Ryu3Department of Spinal Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Neurosurgery, National Health Insurance Service Ilsan Hospital, Yonsei University College of Medicine, Goyang 10444, KoreaDepartment of Spinal Neurosurgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, KoreaDepartment of Neurosurgery, National Health Insurance Service Ilsan Hospital, Yonsei University College of Medicine, Goyang 10444, KoreaDoctors 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.https://www.mdpi.com/2227-9032/10/6/1094bag of visual wordsinfrared thermographylumbosacral radiculopathymachine learning |
| spellingShingle | Gi Nam Kim Ho Yeol Zhang Yong Eun Cho Seung Jun Ryu Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images Healthcare bag of visual words infrared thermography lumbosacral radiculopathy machine learning |
| title | Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images |
| title_full | Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images |
| title_fullStr | Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images |
| title_full_unstemmed | Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images |
| title_short | Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images |
| title_sort | differential screening of herniated lumbar discs based on bag of visual words image classification using digital infrared thermographic images |
| topic | bag of visual words infrared thermography lumbosacral radiculopathy machine learning |
| url | https://www.mdpi.com/2227-9032/10/6/1094 |
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