Clinical and pathological correlates of severity classifications in trigger fingers based on computer-aided image analysis

Tai-Hua Yang, Hsin Chen Chen, Yung Chun Liu, Hui Hsuan Shih, Li-Chieh Kuo, Stephen Cha, Hsiao Bai Yang, Dee Shan Yang, I. Ming Jou, Yung-Nien Sun, Fong-chin Su

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: The treatment of trigger finger so far has heavily relied on clinicians' evaluations for the severity of patients' symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study's aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images.Methods: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed.Results: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen's kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification.Conclusions: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level.

Original languageEnglish
Article number100
JournalBiomedical engineering online
Volume13
Issue number1
DOIs
Publication statusPublished - 2014 Jul 23

Fingerprint

Image analysis
Fingers
Tissue
Analysis of variance (ANOVA)
Analysis of Variance
Physicians
Therapeutics

All Science Journal Classification (ASJC) codes

  • Radiological and Ultrasound Technology
  • Biomaterials
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Yang, Tai-Hua ; Chen, Hsin Chen ; Liu, Yung Chun ; Shih, Hui Hsuan ; Kuo, Li-Chieh ; Cha, Stephen ; Yang, Hsiao Bai ; Yang, Dee Shan ; Jou, I. Ming ; Sun, Yung-Nien ; Su, Fong-chin. / Clinical and pathological correlates of severity classifications in trigger fingers based on computer-aided image analysis. In: Biomedical engineering online. 2014 ; Vol. 13, No. 1.
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abstract = "Background: The treatment of trigger finger so far has heavily relied on clinicians' evaluations for the severity of patients' symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study's aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images.Methods: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed.Results: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen's kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification.Conclusions: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level.",
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Clinical and pathological correlates of severity classifications in trigger fingers based on computer-aided image analysis. / Yang, Tai-Hua; Chen, Hsin Chen; Liu, Yung Chun; Shih, Hui Hsuan; Kuo, Li-Chieh; Cha, Stephen; Yang, Hsiao Bai; Yang, Dee Shan; Jou, I. Ming; Sun, Yung-Nien; Su, Fong-chin.

In: Biomedical engineering online, Vol. 13, No. 1, 100, 23.07.2014.

Research output: Contribution to journalArticle

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AU - Chen, Hsin Chen

AU - Liu, Yung Chun

AU - Shih, Hui Hsuan

AU - Kuo, Li-Chieh

AU - Cha, Stephen

AU - Yang, Hsiao Bai

AU - Yang, Dee Shan

AU - Jou, I. Ming

AU - Sun, Yung-Nien

AU - Su, Fong-chin

PY - 2014/7/23

Y1 - 2014/7/23

N2 - Background: The treatment of trigger finger so far has heavily relied on clinicians' evaluations for the severity of patients' symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study's aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images.Methods: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed.Results: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen's kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification.Conclusions: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level.

AB - Background: The treatment of trigger finger so far has heavily relied on clinicians' evaluations for the severity of patients' symptoms and the functionality of affected fingers. However, there is still a lack of pathological evidence supporting the criteria of clinical evaluations. This study's aim was to correlate clinical classification and pathological changes for trigger finger based on the tissue abnormality observed from microscopic images.Methods: Tissue samples were acquired, and microscopic images were randomly selected and then graded by three pathologists and two physicians, respectively. Moreover, the acquired images were automatically analyzed to derive two quantitative parameters, the size ratio of the abnormal tissue region and the number ratio of the abnormal nuclei, which can reflect tissue abnormality caused by trigger finger. A self-developed image analysis system was used to avoid human subjectivity during the quantification process. Finally, correlations between the quantitative image parameters, pathological grading, and clinical severity classification were assessed.Results: One-way ANOVA tests revealed significant correlations between the image quantification and pathological grading as well as between the image quantification and clinical severity classification. The Cohen's kappa coefficient test also depicted good consistency between pathological grading and clinical severity classification.Conclusions: The criteria of clinical classification were found to be highly associated with the pathological changes of affected tissues. The correlations serve as explicit evidence supporting clinicians in making a treatment strategy of trigger finger. In addition, our proposed computer-aided image analysis system was considered to be a promising and objective approach to determining trigger finger severity at the microscopic level.

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