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

Kim, Gi Nam; Zhang, Ho Yeol; Cho, Young-Eun; Ryu, Seung Jun

doi:10.3390/healthcare10061094

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Consequently, previous attempts to establish reference standards for DITI using systematic reviews based on meta-analysis or machine learning methods have been limited in providing detailed standard DITI values [ 10 , 16 , 17 , 18 ]. Although a recent study suggested a correct differential diagnosis process for Raynaud’s phenomenon in the hand using a deep convolutional neural network, a type of deep learning method, its small data set had limitations [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs

2023

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(1) Background: although digital infrared thermographic imaging (DITI) is used for diverse medical conditions of the upper limbs, no reference standards have been established. This study aims to establish reference standards by analyzing DITI results of the upper limbs. (2) Methods: we recruited 905 healthy Korean adults and conducted thermography on six regions (dorsal arm, ventral arm, lateral arm, medial arm, dorsal hand, and ventral hand region). We analyzed the data based on the proximity of regions of interest (ROIs), sex, and age. (3) Results: the average temperature (°C) and temperature discrepancy between the right and the left sides (ΔT) of each ROI varied significantly (p < 0.001), ranging from 28.45 ± 5.71 to 29.74 ± 5.14 and from 0.01 ± 0.49 to 0.15 ± 0.62, respectively. The temperature decreased towards the distal ROIs compared to proximal ROIs. The average temperatures of the same ROIs were significantly higher for men than women in all regions (p < 0.001). Across all regions, except the dorsal hand region, average temperatures tended to increase with age, particularly in individuals in their 30s and older (p < 0.001). (4) Conclusions: these data could be used as DITI reference standards to identify skin temperature abnormalities of the upper limbs. However, it is important to consider various confounding factors, and further research is required to validate the accuracy of our results under pathological conditions.

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Section: Introductionmentioning

confidence: 99%

Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs

2023

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Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging

et al. 2023

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Pain is subjective and varies among individuals. Doctors determine pain severity based on a patient’s self-reported symptoms. In such situations, a language barrier may prevent patients from expressing their pain accurately, which may cause doctors to underestimate their pain degree. Moreover, patients’ subjective descriptions of pain can determine their eligibility for secondary benefits, as in the case of compensation for traffic or industrial accidents. Therefore, to perform a multiclass prediction of the severity of lumbar radiculopathy, the authors applied digital infrared thermographic imaging (DITI) to a machine-learning (ML) algorithm. The DITI dataset included data from a healthy population and patients with radiculopathy with herniated lumbar discs at the L3/4, L4/5, and L5/S1 levels. The dataset of 1000 patients was split into training and test datasets in a 7:3 ratio to evaluate the model’s performance. For the training dataset, the average accuracy, precision, recall, and F1 score were 0.82, 0.76, 0.72, and 0.74, respectively. For the test dataset, these values were 0.77, 0.71, 0.75, and 0.73, respectively. Applying the ML algorithm to a pain-severity classification using thermographic images will aid in the treatment of lumbosacral radiculopathy and allow providers to monitor the therapeutic effect of interventions through an assessment of physiological evidence.

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

Cited by 2 publications

References 30 publications

Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs

Reference Standards for Digital Infrared Thermography Measuring Surface Temperature of the Upper Limbs

Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging

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