Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints
| Autor: | Tristan Warren, Amber Van Den Moosdijk, Peter van der Putten, Walter van der Weegen, Michiel Siebelt, Dirk Das |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Emergency Medical Services Big data MEDLINE Machine learning computer.software_genre Outcome (game theory) Osteoarthritis Hip Diagnosis Differential Machine Learning 03 medical and health sciences 0302 clinical medicine Text mining Bursitis Predictive Value of Tests Surveys and Questionnaires Medicine Humans Orthopedics and Sports Medicine In patient Medical history 030212 general & internal medicine Medical diagnosis Medical History Taking Aged Orthopedic surgery 030222 orthopedics business.industry General Medicine Middle Aged ROC Curve Tendinopathy Surgery Female Artificial intelligence business computer RD701-811 Research Article |
| Zdroj: | Acta Orthopaedica, Vol 92, Iss 3, Pp 254-257 (2021) Acta Orthopaedica article-version (VoR) Version of Record Acta Orthopaedica, 92(3), 254-257. Taylor & Francis Open Access |
| ISSN: | 1745-3682 1745-3674 |
| Popis: | Background and purpose — Machine learning (ML) techniques are a form of artificial intelligence able to analyze big data. Analyzing the outcome of (digital) questionnaires, ML might recognize different patterns in answers that might relate to different types of pathology. With this study, we investigated the proof-of-principle of ML-based diagnosis in patients with hip complaints using a digital questionnaire and the Kellgren and Lawrence (KL) osteoarthritis score.Patients and methods — 548 patients (> 55 years old) scheduled for consultation of hip complaints were asked to participate in this study and fill in an online questionnaire. Our questionnaire consists of 27 questions related to general history-taking and validated patient-related outcome measures (Oxford Hip Score and a Numeric Rating Scale for pain). 336 fully completed questionnaires were related to their classified diagnosis (either hip osteoarthritis, bursitis or tendinitis, or other pathology). Different AI techniques were used to relate questionnaire outcome and hip diagnoses. Resulting area under the curve (AUC) and classification accuracy (CA) are reported to identify the best scoring AI model. The accuracy of different ML models was compared using questionnaire outcome with and without radiologic KL scores for degree of osteoarthritis.Results — The most accurate ML model for diagnosis of patients with hip complaints was the Random Forest model (AUC 82%, 95% CI 0.78–0.86; CA 69%, CI 0.64–0.74) and most accurate analysis with addition of KL scores was with a Support Vector Machine model (AUC 89%, CI 0.86–0.92; CA 83%, CI 0.79–0.87).Interpretation — Analysis of self-reported online questionnaires related to hip complaints can differentiate between basic hip pathologies. The addition of radiological scores for osteoarthritis further improves these outcomes. |
| Databáze: | OpenAIRE |
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