A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

Abstract

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients’ medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients’ medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient’s statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients’ simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients’ time in hospitals.

Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network

“…Joseph Antony et al [2] have used a method to localize knee joint and classify the joint OA using fully convolutional neural network and obtained better results. Jihye Lim et al [3] have used deep learning neural network for OA detection using subjects" statistical and behavioral data. [9][10].…”

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

“…Although important to initially establish AI system performance compared with human radiologists, it will be useful to make comparisons with a more robust reference standard with arthroscopic or surgical data and incorporate patient outcomes data. A combined AI system incorporating automated radiographic or MRI segmentation, detection, and staging eventually could be paired with an AI system incorporating clinical data 53,54 to provide more reliable outcome predictions for patients. Although we are not there yet, AI may soon play a useful role in the automated detection of cartilage lesions and in the distinction between early stages of OA, difficult tasks for radiologists in both MRI and radiography.…”

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure

“…Recently, machine learning algorithms, which can solve the nonlinear relationship among multi-dimensional variables, have been shown to be effective in prediction, and are being used successfully in various healthcare applications, such as medical diagnosis [22,23] and disease risk prediction [24,25]. Nevertheless, only a very limited number of studies have attempted to adopt machinelearning based data-driven approaches to forecast the demand for healthcare services associated with environmental exposure, and these few studies predominately focused on the application of artificial neural network (ANN) [26][27][28][29].…”