Introduction Osteoarthritis is a leading cause of global disability and is set to worsen with the concurrent rise in rates of obesity and an ageing population [1]. Current clinical solutions are sub-optimal with regards to their invasiveness and outcomes. Orthopaedic biologics is an emerging field that offers alternative and parallel treatment options to address this problem. Determining which patients will benefit most from these novel treatments is key in developing clinical pathways. Methods Our dataset included 329 patients treated with microfragmented fat injection (MFAT) over a 2 year period. Clinicodemographic data was recorded as well as 1-year Oxford Knee Score (OKS). The data was modelled to predict OKS 1-year response using Random Forest Regressors. Gender-bias was mitigated and outliers were hidden from the training model. The model was validated on raw test data and on a subset of patients with Kellgren-Lawrence grade 3 and 4 radiological evidence of arthritis, age greater than 64, preoperative OKS less than or equal to 27 and idiopathic aetiology of arthritis. Results The mean age and mean body mass index (BMI) of patients in our dataset was 66.4 years, 26.9 respectively. 53.5% of patients had Kellgren-Lawrence grade 4 arthritis. The final models RMSE was 6.72, MAE was 5.38 and r-squared was 0.23 on raw test data. An RMSE of 9.77 and MAE of 7.81 was achieved when validating the model on our subset of patients. Wilcoxon signed rank tests found no evidence of predicted results being statistically significantly different to ground truth values (p > 0.05). Preoperative OKS and Kellgren-Lawrence arthritis grade was the most important feature in our model. Discussion Our model is performant and able to predict 1 year OKS response outcome within our set of patients. We have found key features of prediction and would recommend these are researched further to improve model performance. Our dataset does not compare outcomes with other standard treatments. We also do not compare outcomes with other biologic treatments. Ultimately, this research can be used as a tool to benefit both patients and clinicians in a combined decision-making process.
Background Quantum computing (QC) and quantum machine learning (QML) are promising experimental technologies which can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. 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. 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 results on a small validation dataset show that quantum machine learning applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. 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.
Hip osteoarthritis (OA) is a major contributor to reduced quality of life and concomitant disability associated with lost working life months. Intra-articular injection of various biological materials has shown promise in alleviating symptoms and potentially slowing down the degenerative process. Here, we compared the effects of treatment of a cohort of 147 patients suffering from grade 1–4 hip OA; with either micro-fragmented adipose tissue (MFAT), or a combination of MFAT with platelet-rich plasma (PRP). We found significant improvements in both the visual analogue score for pain (VAS) and Oxford hip score (OHS) that were similar for both treatments with over 60% having an improvement in the VAS score of 20 points or more. These results suggest a positive role for intra-articular injection of MFAT + PRP as a treatment for hip osteoarthritis which may be important particularly in low body mass index (BMI) patients where the difficulty in obtaining sufficient MFAT for treatment could be offset by using this combination of biologicals.
Background The goal of our 4-phase research project was to test if a machine-learning-based loan screening application (5D) could detect bad loans: 1) in a subset of non-performing loans, and; 2) in a set of performing and non-performing loans, subject to the following constraints: a) utilize a minimal-optimal number of publicly available features unrelated to the credit history, gender, race or ethnicity of the borrower (BiMOPT features); b) comply with the European Banking Authority and EU Commission AI HLEG principles on trustworthy Artificial Intelligence (AI). Methods All datasets have been anonymized and pseudoanonymized. In Phase 0 we selected a subset of 10 BiMOPT features out of a total of 84 features; in Phase I we trained 5D to detect bad loans in a historical dataset extracted from a mandatory report to the Bank of Italy consisting of 7,289 non-performing loans (NPLs) closed in the period 2010-2021; in Phase II we assessed the baseline performance of 5D on a distinct validation dataset consisting of an active portolio of 63,763 outstanding loans (performing and non-performing) for a total financed value of over EUR 11.5 billion as of December 31, 2021; in Phase III we will monitor the baseline performance for a period of 5 years (2023-27) to assess the prospective real-world bias-mitigation and performance of the 5D system and its utility in credit and fintech institutions Results 5D correctly detected 1,461 bad loans out of a total of 1,613 (Sensitivity = 0.91, Prevalence = 0.0253;, Positive Predictive Value = 0.19), and correctly classified 55,866 out of the other 62,150 exposures (Specificity = 0.90, Negative Predictive Value = 0.997). Interpretation Our preliminary results support the hypothesis that Big Data & Advanced Analytics applications based on AI can mitigate bias and improve consumer protection in the loan screening process without compromising the efficacy of the credit risk assessment. Further validation is required to assess the prospective performance and utility of 5D in credit and fintech institutions. Funding QuantumSPEKTRAL and Alba Leasing.
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