Predicting osteoarthritis in adults using statistical data mining and machine learning

Machine Learning Models for Prediction of Progression of Knee Osteoarthritis: A Comprehensive Analysis

Prediction of the progression of knee osteoarthritis (KOA) is a very challenging task. Early identification of risk factors plays a vital role in diagnosing KOA. Thus, machine learning models are used to predict the progression of KOA. The purpose of the present study is to find out the efficacy of various machine learning models to identify the progression of KOA. A comprehensive literature search was conducted in international databases like Google Scholar, PubMed, Web of Science, and Scopus. Studies published from the year 2010 to May 2023 on the machine learning approach to diagnose KOA were included in the study. A total of 15 studies were selected and analyzed which included machine learning as an approach to diagnose KOA. The present study found that machine learning methods are the best methods to diagnose KOA early. Various methods like deep learning, machine learning, convolutional neural network (CNN), and multi-layer perceptron showed good accuracy in diagnosing its progression. The machine learning approach has attracted significant interest from scientists and researchers and has led to a new automated approach to diagnose KOA, which will help in designing treatment approaches.

Development and validation of a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China

Background

Considering its high prevalence, estimating the risk of arthritis in middle-aged and older Chinese adults is of particular interest. This study was conducted to develop a risk prediction model for arthritis in community-dwelling middle-aged and older adults in China.

Methods

Our study included a total of 9599 participants utilising data from the China Health and Retirement Longitudinal Study (CHARLS). Participants were randomly assigned to training and validation groups at a 7:3 ratio. Univariate and multivariate binary logistic regression analyses were used to identify the potential predictors of arthritis. Based on the results of the multivariate binary logistic regression, a nomogram was constructed, and its predictive performance was evaluated using the receiver operating characteristic (ROC) curve. The accuracy and discrimination ability were assessed using calibration curve analysis, while decision curve analysis (DCA) was performed to evaluate the net clinical benefit rate.

Results

A total of 9599 participants were included in the study, of which 6716 and 2883 were assigned to the training and validation groups, respectively. A nomogram was constructed to include age, hypertension, heart diseases, gender, sleep time, body mass index (BMI), residence address, the parts of joint pain, and trouble with body pains. The results of the ROC curve suggested that the prediction model had a moderate discrimination ability (AUC >0.7). The calibration curve of the prediction model demonstrated a good predictive accuracy. The DCA curves revealed a favourable net benefit for the prediction model.

Conclusions

The predictive model demonstrated good discrimination, calibration, and clinical validity, and can help community physicians and clinicians to preliminarily assess the risk of arthritis in middle-aged and older community-dwelling adults.

Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

PredictMed-epilepsy: A multi-agent based system for epilepsy detection and prediction in neuropediatrics

BACKGROUND AND OBJECTIVE

Epileptic seizures are associated with a higher incidence of Developmental Disabilities and Cerebral Palsy. Early evaluation and management of epilepsy is strongly recommended. We propose and discuss an application to predict epilespy (PredictMed-Epilepsy) and seizures via a deep-learning module (PredictMed-Seizures) encompassed within a multi-agent based healthcare system (PredictMed-MHS); this system is meant, in perspective, to be integrated into a clinical decision support system (PredictMed-CDSS). PredictMed-Epilespy, in particular, aims to identify factors associated with epilepsy in children with Developmental Disabilities and Cerebral Palsy by using a prediction-learning model named PredictMed. PredictMed-epilespy methods: We performed a longitudinal, multicenter, double-blinded, descriptive study of one hundred and two children with Developmental Disabilities and Cerebral Palsy (58 males, 44 females; 65 inpatients, 37 outpatients; 72 had epilepsy - 22 of intractable epilepsy, age: 16.6±1.2y, range: 12-18y). Data from 2005 to 2021 on Cerebral Palsy etiology, diagnosis, type of epilepsy and spasticity, clinical history, communication abilities, behaviors, intellectual disability, motor skills, and eating and drinking abilities were collected. The machine-learning model PredictMed was exploited to identify factors associated with epilepsy. The guidelines of the "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis" Statement (TRIPOD) were followed. PredictMed-epilepsy results: Cerebral Palsy etiology [(prenatal > perinatal > postnatal causes) p=0.036], scoliosis (p=0.048), communication (p=0.018) and feeding disorders (p=0.002), poor motor function (p<0.001), intellectual disabilities (p=0.007), and type of spasticity [(quadriplegia/triplegia > diplegia > hemiplegia), p=0.002)] were associated with having epilepsy. The prediction model scored an average of 82% of accuracy, sensitivity, and specificity. Thus, PredictMed defined the computational phenotype of children with Developmental Disabilities/Cerebral Palsy at risk of epilepsy. Novel contribution of the work: We have been developing and we have prototypically implemented a Multi-Agent Systems (MAS) that encapsulates the PredictMed-Epilepsy module. More specifically, we have implemented the Patient Observing MAS (PoMAS), which, as a novelty w.r.t. the existing literature, includes a complex event processing module that provides real-time detention of short- and long-term events related to the patient's condition.

Prediction Model for Identifying Computational Phenotypes of Children with Cerebral Palsy Needing Neurotoxin Treatments

Factors associated with neurotoxin treatments in children with cerebral palsy (CP) are poorly studied. We developed and externally validated a prediction model to identify the prognostic phenotype of children with CP who require neurotoxin injections. We conducted a longitudinal, international, multicenter, double-blind descriptive study of 165 children with CP (mean age 16.5 ± 1.2 years, range 12−18 years) with and without neurotoxin treatments. We collected functional and clinical data from 2005 to 2020, entered them into the BTX-PredictMed machine-learning model, and followed the guidelines, “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis”. In the univariate analysis, neuromuscular scoliosis (p = 0.0014), equines foot (p < 0.001) and type of etiology (prenatal > peri/postnatal causes, p = 0.05) were linked with neurotoxin treatments. In the multivariate analysis, upper limbs (p < 0.001) and trunk muscle tone disorders (p = 0.02), the presence of spasticity (p = 0.01), dystonia (p = 0.004), and hip dysplasia (p = 0.005) were strongly associated with neurotoxin injections; and the average accuracy, sensitivity, and specificity was 75%. These results have helped us identify, with good accuracy, the clinical features of prognostic phenotypes of subjects likely to require neurotoxin injections.