Öz
Amaç: Karpal tünel sendromu (KTS), median sinirin karpal tünelde sıkışması sonucu en sık görülen tuzak nöropatisidir. Elde edilen veriler sonucunda hastada mevcut KTS kliniği hafif, orta ve ağır olarak gradelenir. KTS derecelendirmesinde klinik tanıda yapay zeka kullanımının etkinliğini göstermeyi amaçladık.
Yöntem: Çalışmamızda KTS ön tanısı ile başvurmuş ve electroneuromyography yapılmış olan 315 bireyin, demografik ve electroneuromyography sonuçlarından elde edilmiş sinir ileti verileri kullanılmıştır. Sınıflandırma işlemlerinde makine öğrenmesi algoritmalarından Topluluk, Destek Vektör Makinesi, K-En Yakın Komşu ve Karar Ağacı algoritmaları kullanılmıştır. %10 bekletme doğrulaması kullanılmış ve öğrenme oranı 0.1 olarak belirlenmiştir. Sınıflandırma sonucunda doğruluk, kesinlik, duyarlılık, özgüllük ve F1-skor performans değerleri elde edilmiştir.
Bulgular: Çalışmamızın sonucunda 0 sınıfında en iyi tahmini Destek Vektör Makinesi, en kötü tahmini K-En Yakın Komşu yapmıştır. 1. sınıfda en iyi tahmin Destek Vektör Makinesine aittir. 2. ve 3. sınıflarda en iyi tahmini Topluluk ve Karar Ağacı yapmıştır. Çalışmamızda, genel başarı oranı en iyi algoritma % 93,55 ile Destek Vektör Makinesidir.
Sonuç: Makine öğrenme algoritma modellerinin tutarlı bir şekilde daha iyi tahmin sonuçları sağladığını ve doktorlara KTS'nin tıbbi tedavi yöntemini belirlemede yardımcı olacağını gösterdi. Yapay zeka teknikleri, klinisyenlerin kaliteli sağlık hizmeti sunmalarına yardımcı olan güvenilir yöntemlerdir.
Anahtar Kelimeler
karpal tünel sendromu elektromiyografi yapay zeka derecelendirme
Kaynakça
- Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artifcial intelligence (AI) and global health: howcan AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3(4):e000798. doi:10.1136/bmjgh-2018-000798
- Pedersen M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD. Artificial intelligence for clinical decision support in neurology. Brain communications. 2020;2(2):fcaa096. doi:10.1093/braincomms/fcaa096
- Schweingruber N, Gerloff C. Künstliche Intelligenz in der Neurointensivmedizin. Der Nervenarzt. 2021;92(2),115-126. doi:10.1007/s00115-020-01050-4
- Patel, UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. Journal of neurology. 2021;268(5),1623-1642. doi:10.1007/s00415-019-09518-3
- Genova A, Dix O, Saefan A, Thakur M, Hassan A. Carpal tunnel syndrome: a review of the literature. Cureus. 2020;12(3). doi:10.7759/cureus.7333
- Atroshi I, Gummesson C, Johnson R, Ornstein E, Ranstam J, Rosen I. Prevalence of carpal tunnel syndrome in a general population. JAMA. 1999;282:153-158. doi:10.1001/jama.282.2.153
- Padua L, LoMonaco M, Padua R. Neurophysiological classification of carpal tunnel syndrome: assessment of 600 symptomatic hands. Ital J Neurol Sci. 1997;18:145-50.
- Aulisa L, Tamburrelli F, Padua R, Romanini E, Lo Monaco M, Padua L. Carpal tunnel syndrome: indication for surgical treatment based on the electrophysiological study. J Hand Surg. 1998;23:687-91.
- Premoselli S, Sioli P, Grossi A, Cerri C. Neutral wrist splinting in carpal tunnel syndrome: a 3- and 6-months clinical and neurophysiologic follow-up evaluation of night only splint therapy. Eura Medicophys. 2006;42(2):121-126.
- Karsidag S, Sahin S, Hacikerim Karsidag S, Ayala S. Long term and frequent electrophysiological observation in carpal tunnel syndrome. Eura Medicophys. 2007;43(3):327-32.
- Iida JI, Hirabayashi H, Nakase H, Sakaki T. Carpal tunnel syndrome: electrophysiological grading and surgical results by minimum incision open carpal tunnel release. Neurologia medico-chirurgica 2008;48(12):54-559. doi:10.2176/nmc.48.554
- Stevens JC. AAEM minimonograph# 26: the electrodiagnosis of carpal tunnel syndrome. Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine. 1997;20(12):1477-1486. doi:10.1002
- Wei Y, Gu F, Zhang W. A two-phase iterative machine learning method in identifying mechanical biomarkers of peripheral neuropathy. Expert Systems with Applications. 2021;169:114333. doi:10.1016/j.eswa.2020.114333
- Lui YW, Chang PD, Zaharchuk G, et al. Artificial intelligence in neuroradiology: Current status and future directions. American Journal of Neuroradiology. 2020;41(8):E52-E59. doi:10.3174/ajnr.A6681
- Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present, and future. Stroke and vascular neurology. 2017;2(4). doi:10.1136/svn-2017-000101
- Cramer JS. The origins of logistic regression. 2002.
- Subasi A, Mian Qaisar S. The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface. Journal of Healthcare Engineering. 2021. doi:10.1155/2021/1970769
- Chilla GS, Yeow LY, Chew QH, Sim K, Prakash KN. Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. Scientific reports. 2022;12(1):1-11. doi:10.1038/s41598-022-06651-4
- Yousefi J, Hamilton-Wright A. Characterizing EMG data using machine-learning tools. Computers in biology and medicine. 2014;51:1-13. doi:10.1016/j.compbiomed.2014.04.018
- Wang Z, Dreyer F, Pulvermüller F, et al. Support vector machine-based aphasia classification of transcranial magnetic stimulation language mapping in brain tumor patients. NeuroImage: Clinical. 2021;29:102536. doi:10.1016/j.nicl.2020.102536
- Demirel Ş, Yakut SG. Karar Ağacı Algoritmaları ve Çocuk İşçiliği Üzerine Bir Uygulama. Sosyal Bilimler Araştırma Dergisi. 2019;8(4):52-65.
- Yaman E, Subasi A. Comparison of bagging and boosting ensemble machine learning methods for automated EMG signal classification. BioMed research international. 2019. doi:10.1155/2019/9152506
- Aksu MÇ, Karaman E. Karar Ağaçları ile Bir Web Sitesinde Link Analizi ve Tespiti. Acta Infologica. 2017;1(2):84-91.
- Yadav S, Shukla S. Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In the 2016 IEEE 6th International conference on advanced computing (IACC). 2016;78-83. IEEE. doi:10.1109/IACC.2016.25
- World Health Organization. Obesity and overweight. Accessed at https://who.int/news-room/fact-sheets/detail/ obesity-and-overweight on May 6, 2020.
- Padua L, LoMonaco M, Gregori B, Valente EM, Padua R, Tonali P. Neurophysiological classification and sensitivity in 500 carpal tunnel syndrome hands. Acta Neurologica Scandinavica. 1997;96(4):211-217. doi:10.1111/j.1600-0404.1997.tb00271.x
- Szabo RM, Slater Jr, RR., Farver TB, Stanton DB, Sharman WK. The value of diagnostic testing in carpal tunnel syndrome. The Journal of hand surgery. 1999;24(4):704-714. doi:10.1053/jhsu.1999.0704
- Kunhimangalam R, Ovallath S, Joseph PK. A novel fuzzy expert system for the identification of the severity of carpal tunnel syndrome. BioMed research international. 2013. doi:10.1155/2013/846780
- Eslami S, Fadaei B, Baniasadi M, Yavari P. Clinical presentation of carpal tunnel syndrome with different severity: a cross-sectional study. American Journal of Clinical and Experimental Immunology. 2019;8(4):32.
- Hirani S. A study to further develop and refine the carpal tunnel syndrome (CTS) nerve conduction grading tool. BMC Musculoskeletal Disorders. 2019;20(1):1-7. doi:10.1186/s12891-019-2928-y
- Park D., Kim B.H., Lee S.E., et al. Machine learning-based approach for disease severity classification of carpal tunnel syndrome. Scientific Reports. 2021;11(1):1-10. doi:10.1038/s41598-021-97043-7
- Faeghi F, Ardakani AA, Acharya UR, et al. Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists’ assessment. European Journal of Radiology. 2021;136:109518. doi:10.1016/j.ejrad.2020.109518
- Vasta R, Cerasa A, Sarica A, et al. The application of artifcial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures. Epilepsy Behav. 2018;87:167–172. doi:10.1016/j.yebeh.2018.09.008
- Arani LA, Hosseini A, Asadi F, Masoud SA, Nazemi E. Intelligent computer systems for multiple sclerosis diagnosis: a systematic review of reasoning techniques and methods. Acta Inf Med. 2018;26(4):258–264. doi:10.5455/aim.2018.26.258-264
- Brzezicki M A, Kobetić MD, Neumann S, Pennington C. Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement. Advances in Medical Sciences. 2019;64(2):292-302. doi:10.1016/j.advms.2019.03.002
Öz
Objective: The most common entrapment neuropathy seen by the clinician is Carpal tunnel syndrome (CTS). CTS is graded as mild, moderate, and severe according to the results obtained on electroneuromyography (ENMG) by clinicians. We aimed to show the effectiveness of the use of artificial intelligence in clinical diagnosis in the grading of CTS.
Methods: In our study, the data of 315 people with a pre-diagnosis of CTS were used and classified into four classes based on AI as CTS grade. Machine Learning (ML) algorithms Ensemble, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (Tree) algorithms were used in classification processes. 10% Hold-out validation was used and the learning rate was determined as 0.1. As a result of the classification, accuracy, precision, sensitivity, specificity, and F1-score performance values were obtained.
Results: SVM made the best estimation and KNN made the worst estimation in the 0 class. The best estimate in class 1 belongs to the Support Vector Machine. Ensemble and Tree made the best guesses in the 2nd and 3rd grades. In our study, the best algorithm with an overall success rate is SVM with 93.55%.
Conclusions: The results showed that ML algorithm models consistently provided better predictive results and would assist physicians in determining the medical treatment modality of CTS. Artificial intelligence (AI) techniques are reliable methods that assist clinicians to deliver quality healthcare.
Anahtar Kelimeler
carpal tunnel syndrome electromyography artificial intelligence grading
Kaynakça
- Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artifcial intelligence (AI) and global health: howcan AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3(4):e000798. doi:10.1136/bmjgh-2018-000798
- Pedersen M, Verspoor K, Jenkinson M, Law M, Abbott DF, Jackson GD. Artificial intelligence for clinical decision support in neurology. Brain communications. 2020;2(2):fcaa096. doi:10.1093/braincomms/fcaa096
- Schweingruber N, Gerloff C. Künstliche Intelligenz in der Neurointensivmedizin. Der Nervenarzt. 2021;92(2),115-126. doi:10.1007/s00115-020-01050-4
- Patel, UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. Journal of neurology. 2021;268(5),1623-1642. doi:10.1007/s00415-019-09518-3
- Genova A, Dix O, Saefan A, Thakur M, Hassan A. Carpal tunnel syndrome: a review of the literature. Cureus. 2020;12(3). doi:10.7759/cureus.7333
- Atroshi I, Gummesson C, Johnson R, Ornstein E, Ranstam J, Rosen I. Prevalence of carpal tunnel syndrome in a general population. JAMA. 1999;282:153-158. doi:10.1001/jama.282.2.153
- Padua L, LoMonaco M, Padua R. Neurophysiological classification of carpal tunnel syndrome: assessment of 600 symptomatic hands. Ital J Neurol Sci. 1997;18:145-50.
- Aulisa L, Tamburrelli F, Padua R, Romanini E, Lo Monaco M, Padua L. Carpal tunnel syndrome: indication for surgical treatment based on the electrophysiological study. J Hand Surg. 1998;23:687-91.
- Premoselli S, Sioli P, Grossi A, Cerri C. Neutral wrist splinting in carpal tunnel syndrome: a 3- and 6-months clinical and neurophysiologic follow-up evaluation of night only splint therapy. Eura Medicophys. 2006;42(2):121-126.
- Karsidag S, Sahin S, Hacikerim Karsidag S, Ayala S. Long term and frequent electrophysiological observation in carpal tunnel syndrome. Eura Medicophys. 2007;43(3):327-32.
- Iida JI, Hirabayashi H, Nakase H, Sakaki T. Carpal tunnel syndrome: electrophysiological grading and surgical results by minimum incision open carpal tunnel release. Neurologia medico-chirurgica 2008;48(12):54-559. doi:10.2176/nmc.48.554
- Stevens JC. AAEM minimonograph# 26: the electrodiagnosis of carpal tunnel syndrome. Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine. 1997;20(12):1477-1486. doi:10.1002
- Wei Y, Gu F, Zhang W. A two-phase iterative machine learning method in identifying mechanical biomarkers of peripheral neuropathy. Expert Systems with Applications. 2021;169:114333. doi:10.1016/j.eswa.2020.114333
- Lui YW, Chang PD, Zaharchuk G, et al. Artificial intelligence in neuroradiology: Current status and future directions. American Journal of Neuroradiology. 2020;41(8):E52-E59. doi:10.3174/ajnr.A6681
- Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present, and future. Stroke and vascular neurology. 2017;2(4). doi:10.1136/svn-2017-000101
- Cramer JS. The origins of logistic regression. 2002.
- Subasi A, Mian Qaisar S. The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface. Journal of Healthcare Engineering. 2021. doi:10.1155/2021/1970769
- Chilla GS, Yeow LY, Chew QH, Sim K, Prakash KN. Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. Scientific reports. 2022;12(1):1-11. doi:10.1038/s41598-022-06651-4
- Yousefi J, Hamilton-Wright A. Characterizing EMG data using machine-learning tools. Computers in biology and medicine. 2014;51:1-13. doi:10.1016/j.compbiomed.2014.04.018
- Wang Z, Dreyer F, Pulvermüller F, et al. Support vector machine-based aphasia classification of transcranial magnetic stimulation language mapping in brain tumor patients. NeuroImage: Clinical. 2021;29:102536. doi:10.1016/j.nicl.2020.102536
- Demirel Ş, Yakut SG. Karar Ağacı Algoritmaları ve Çocuk İşçiliği Üzerine Bir Uygulama. Sosyal Bilimler Araştırma Dergisi. 2019;8(4):52-65.
- Yaman E, Subasi A. Comparison of bagging and boosting ensemble machine learning methods for automated EMG signal classification. BioMed research international. 2019. doi:10.1155/2019/9152506
- Aksu MÇ, Karaman E. Karar Ağaçları ile Bir Web Sitesinde Link Analizi ve Tespiti. Acta Infologica. 2017;1(2):84-91.
- Yadav S, Shukla S. Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In the 2016 IEEE 6th International conference on advanced computing (IACC). 2016;78-83. IEEE. doi:10.1109/IACC.2016.25
- World Health Organization. Obesity and overweight. Accessed at https://who.int/news-room/fact-sheets/detail/ obesity-and-overweight on May 6, 2020.
- Padua L, LoMonaco M, Gregori B, Valente EM, Padua R, Tonali P. Neurophysiological classification and sensitivity in 500 carpal tunnel syndrome hands. Acta Neurologica Scandinavica. 1997;96(4):211-217. doi:10.1111/j.1600-0404.1997.tb00271.x
- Szabo RM, Slater Jr, RR., Farver TB, Stanton DB, Sharman WK. The value of diagnostic testing in carpal tunnel syndrome. The Journal of hand surgery. 1999;24(4):704-714. doi:10.1053/jhsu.1999.0704
- Kunhimangalam R, Ovallath S, Joseph PK. A novel fuzzy expert system for the identification of the severity of carpal tunnel syndrome. BioMed research international. 2013. doi:10.1155/2013/846780
- Eslami S, Fadaei B, Baniasadi M, Yavari P. Clinical presentation of carpal tunnel syndrome with different severity: a cross-sectional study. American Journal of Clinical and Experimental Immunology. 2019;8(4):32.
- Hirani S. A study to further develop and refine the carpal tunnel syndrome (CTS) nerve conduction grading tool. BMC Musculoskeletal Disorders. 2019;20(1):1-7. doi:10.1186/s12891-019-2928-y
- Park D., Kim B.H., Lee S.E., et al. Machine learning-based approach for disease severity classification of carpal tunnel syndrome. Scientific Reports. 2021;11(1):1-10. doi:10.1038/s41598-021-97043-7
- Faeghi F, Ardakani AA, Acharya UR, et al. Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists’ assessment. European Journal of Radiology. 2021;136:109518. doi:10.1016/j.ejrad.2020.109518
- Vasta R, Cerasa A, Sarica A, et al. The application of artifcial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures. Epilepsy Behav. 2018;87:167–172. doi:10.1016/j.yebeh.2018.09.008
- Arani LA, Hosseini A, Asadi F, Masoud SA, Nazemi E. Intelligent computer systems for multiple sclerosis diagnosis: a systematic review of reasoning techniques and methods. Acta Inf Med. 2018;26(4):258–264. doi:10.5455/aim.2018.26.258-264
- Brzezicki M A, Kobetić MD, Neumann S, Pennington C. Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement. Advances in Medical Sciences. 2019;64(2):292-302. doi:10.1016/j.advms.2019.03.002
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Nöroloji ve Nöromüsküler Hastalıklar |
| Bölüm | Araştırma Makaleleri |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2023 |
| Gönderilme Tarihi | 27 Ekim 2022 |
| Kabul Tarihi | 29 Mart 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 6 Sayı: 2 |
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