A quantum-enhanced precision medicine application to support data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis: development and preliminary validation of precisionKNEE_QNN

Abstract

Abstract Background The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the features that predict individual response remains a challenge. Variational quantum-classical and quantum machine learning (QML) algorithms based on parameterized quantum circuits (PQC) are promising experimental technologies which can improve the efficiency of precision medicine clinical decision support systems (CDSS) based on real-world data stored in large unstructured databases. In this paper we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methods Following patients’ consent and Research Ethics Committee approval, we collected clinico-demographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥ 3, OKS ≤ 27, Age ≥ 64 and idiopathic aetiology of arthritis) treated over a 2 year period with a single injection of microfragmented fat. Gender classes were balanced (76 M, 94 F) to mitigate gender bias. A patient with an improvement ≥ 7 OKS has been considered a Responder. We trained our QNN Classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 R, 40 NR) in pain and function at 1 year. Outliers were hidden from the training dataset but not from the validation set. We ran our QNN Classifier on a IBM quantum simulator to reduce the error due to noise. Results We tested our QNN Classifier on a randomly selected test subset of 57 patients (34 R, 23 NR) including outliers. The No Information Rate was equal to 0.59. Our application correctly classified 28 Responders out of 34 and 6 non-Responders out of 23 (Sensitivity = 0.82, Specificity = 0.26, F1 Statistic= 0.71). The Positive (LR+) and Negative (LR-) Likelihood Ratios were respectively 1.11 and 0.68. The Diagnostic Odds Ratio (DOR) was equal to 2. Conclusions Preliminary clinical and technical results of a QNN Classifier tested on a relatively small knee osteoarthritis dataset show that quantum machine learning applied to data-driven clinical decisions is a promising technology. Our results need further research validation with larger, real-world unstructured datasets, and clinical validation with an AI Clinical Trial to test model efficacy, safety, clinical significance and relevance at a public health level.

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