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Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

ABSTRACT: 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.

SUBMITTER: Siebelt M

PROVIDER: S-EPMC8231380 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy.Study design and methodsWe conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output.ResultsCompared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85.InterpretationOur machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.

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Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints.

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