Automated Prediction of Osteoarthritis Level in Human Osteochondral Tissue Using Histopathological Images

Abstract

Osteoarthritis (OA) is the most common arthritis and the leading cause of lower extremity disability in older adults. Understanding OA progression is important in the development of patient-specific therapeutic techniques at the early stage of OA rather than at the end stage. Histopathology scoring systems are usually used to evaluate OA progress and the mechanisms involved in the development of OA. This study aims to classify the histopathological images of cartilage specimens automatically, using artificial intelligence algorithms. Hematoxylin and eosin (HE)- and safranin O and fast green (SafO)-stained images of human cartilage specimens were divided into early, mild, moderate, and severe OA. Five pre-trained convolutional networks (DarkNet-19, MobileNet, ResNet-101, NasNet) were utilized to extract the twenty features from the last fully connected layers for both scenarios of SafO and HE. Principal component analysis (PCA) and ant lion optimization (ALO) were utilized to obtain the best-weighted features. The support vector machine classifier was trained and tested based on the selected descriptors to achieve the highest accuracies of 98.04% and 97.03% in HE and SafO, respectively. Using the ALO algorithm, the F1 scores were 0.97, 0.991, 1, and 1 for the HE images and 1, 0.991, 0.97, and 1 for the SafO images for the early, mild, moderate, and severe classes, respectively. This algorithm may be a useful tool for researchers to evaluate the histopathological images of OA without the need for experts in histopathology scoring systems or the need to train new experts. Incorporating automated deep features could help to improve the characterization and understanding of OA progression and development.

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images

“…Ant lions, specifically the elite (best) ant lion, influence the random movements of ants. To ensure the variety and optimization ability of the algorithm, ants will change their positions according to ant lion and elite ant lion [37,38].…”

“…It is employed to evaluate a classification model's effectiveness. With the measurement of performance metrics like accuracy, precision, recall, and F1-score, it can be utilized to assess a classification model's effectiveness [38]. Figure 14 illustrates the results of the confusion matrix of RF with ALO weighting method.…”

Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications

“… 25 However, this method relies more on predicting peaks and combining the MS/MS dataset. Artificial intelligence and deep learning algorithms have become increasingly prevalent in the standard categorization of OA by combining data such as histopathological staining, 133 X-rays, 134 MRI, 135 , and blood tests. 136 However, hyperspectral and molecular imaging as a potential application area for these technologies has not been explored and potentially provides a significant opportunity for innovation and research.…”