Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans

Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

A review of arthritis diagnosis techniques in artificial intelligence era: Current trends and research challenges

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis, which is a chronic disease affecting young to aged population. This paper provides a survey of recent and the most representative deep learning techniques (published between 2018 to 2020) for the diagnosis of osteoarthritis and rheumatoid arthritis. The paper also reviews traditional machine learning methods (published 2015 onward) and their application for the diagnosis of these diseases. The paper identifies open problems and research gaps. We believe that deep learning can assist general practitioners and consultants to predict the course of the disease, make treatment propositions and appraise their potential benefits

Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.Comment: 29 pages, 10 figure

Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.Comment: 26 pages, 5 figure

Automatic knee joint space measurement from plain radiographs

Abstract. Knee osteoarthritis is a common joint disease and one of the leading causes of disability. The disease is characterized by loss of articular cartilage and bone remodeling. Tissue deformations eventually lead to joint space narrowing which can be detected from plain radiographs. Joint space narrowing is typically measured by an experienced radiologist manually, which can be time consuming and error prone process. The aim of this study was to develop and evaluate a fully automatic joint space width measurement method for bilateral knee radiographs. The knee joint was localized from the x-ray images using template matching and the joint space was delineated using active shape model (ASM). Two different automatic joint space measurement methods were tested and the results were validated against manual measurements performed by an experienced researcher. The first joint space width measurements were done by binarizing the joint space and measuring the local thickness of the binary mask using disk fitting. The second method classified bone pixels to tibia and femur. Classification was based on the ASM delineation. Nearest neighbors between femur and tibia were then used to find the joint space width. An automatic method for tibial region of interest (ROI) selection was also implemented. The algorithms used in this thesis were also made publicly available online. The automatically obtained joint space widths were in line with manual measurements. Higher accuracy was obtained using the disk fitting algorithm. Automatic Tibial ROI selection was accurate, although the orientation of the joint was ignored in this study. An open source software with a simple graphical user interface and visualization tools was also developed. Computationally efficient and easily explainable methods were utilized in order to improve accessibility and transparency of computer assisted diagnosis of knee osteoarthritis.Tiivistelmä. Polvinivelrikko on eräs yleisimpiä niveltauteja sekä yksi merkittävimmistä liikuntavammojen aiheuttajista. Nivelrikolle ominaisia piirteitä ovat nivelruston vaurioituminen ja muutokset nivelrustonalaisessa luussa. Kudosten muutokset ja vauriot johtavat lopulta niveltilan kaventumiseen, mikä voidaan havaita röntgenkuvista. Tavallisesti kokenut radiologi tekee niveltilan mittaukset manuaalisesti, mikä vaatii usein paljon aikaa ja on lisäksi virhealtis prosessi. Tämän tutkielman tavoitteena oli kehittää täysin automaattinen niveltilan mittausmenetelmä bilateraalisille polven röntgenkuville. Polvinivel paikallistettiin röntgenkuvista muotoon perustuvalla hahmontunnistuksella ja nivelväli rajattiin käyttämällä aktiivista muodon sovitusta (active shape model, ASM). Nivelvälin mittaukseen käytettiin kahta eri menetelmää, joita verrattiin kokeneen tutkijan tekemiin manuaalisiin mittauksiin. Ensimmäinen nivelvälin mittausmenetelmä sovitti ympyränmuotoisia maskeja niveltilasta tehtyyn binäärimaskiin. Toinen mittausmenetelmä luokitteli luuhun kuuluvat pikselit sääri- ja reisiluuhun. Luokittelu perustui aikaisemmin tehtyyn automaattiseen nivelvälin rajaukseen. Nivelvälin mittaukseen käytettiin lähimpiä naapuripikseleitä sääri- ja reisiluusta. Työssä kehitettiin myös menetelmä automaattiseen sääriluun mielenkiintoalueiden (region of interest, ROI) valintaan. Käytetyt algoritmit ovat julkisesti saatavilla verkossa. Automaattiset nivelväli mittaukset vastasivat manuaalisia mittauksia hyvin. Parempi tarkkuus saatiin käyttämällä ympyrän sovitusta hyödyntävää algoritmia nivelvälin mittaukseen. Sääriluun mielenkiintoalueet onnistuttiin määrittämään automaattisesti, tosin nivelen orientaatiota ei huomioitu tässä työssä. Lisäksi kehitettiin avoimen lähdekoodin ohjelmisto yksinkertaisella graafisella käyttöliittymällä ja visualisointityökaluilla. Työssä käytettiin laskennallisesti tehokkaita ja helposti selitettäviä menetelmiä, mikä edesauttaa tietokoneavusteisen menetelmien käyttöä polvinivelrikon tutkimuksessa