Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. Besides this, we present attention maps highlighting the radiological features affecting the network decision. Such information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.

Automatic medical diagnosis is an emerging center of interest in computer vision as it provides unobtrusive objective information on a patient's condition. The face, as a mirror of health status, can reveal symptomatic indications of specific diseases. Thus, the detection of facial abnormalities or atypical features is at upmost importance when it comes to medical diagnostics. This survey aims to give an overview of the recent developments in medical diagnostics from facial images based on computer vision methods. Various approaches have been considered to assess facial symptoms and to eventually provide further help to the practitioners. However, the developed tools are still seldom used in clinical practice, since their reliability is still a concern due to the lack of clinical validation of the methodologies and their inadequate applicability. Nonetheless, efforts are being made to provide robust solutions suitable for healthcare environments, by dealing with practical issues such as real-time assessment or patients positioning. This survey provides an updated collection of the most relevant and innovative solutions in facial images analysis. The findings show that with the help of computer vision methods, over 30 medical conditions can be preliminarily diagnosed from the automatic detection of some of their symptoms. Furthermore, future perspectives such as the need for interdisciplinary collaboration and collecting publicly available databases are highlighted.

Osteoarthritis (OA) has often regarded as a disease of articular cartilage only. New evidence has shifted the paradigm towards a system biology approach, where also the surrounding tissue, especially bone is studied more vigorously. However, the histological features of subchondral bone are only poorly characterized in current histological grading scales of OA. The aim of this study is to specifically characterize histological changes occurring in subchondral bone at different stages of OA and propose a simple grading system for them. 20 patients undergoing total knee replacement surgery were randomly selected for the study and series of osteochondral samples were harvested from the tibial plateaus for histological analysis. Cartilage degeneration was assessed using the standardized OARSI grading system, while a novel four-stage grading system was developed to illustrate the changes in subchondral bone. Subchondral bone histology was further quantitatively analyzed by measuring the thickness of uncalcified and calcified cartilage as well as subchondral bone plate. Furthermore, internal structure of calcified cartilage-bone interface was characterized utilizing local binary patterns (LBP) based method. The histological appearance of subchondral bone changed drastically in correlation with the OARSI grading of cartilage degeneration. As the cartilage layer thickness decreases the subchondral plate thickness and disorientation, as measured with LBP, increases. Calcified cartilage thickness was highest in samples with moderate OA. The proposed grading system for subchondral bone has significant relationship with the corresponding OARSI grading for cartilage. Our results suggest that subchondral bone remodeling is a fundamental factor already in early stages of cartilage degeneration.

Despite increasing evidence that subchondral bone contributes to osteoarthritis (OA) pathogenesis, little is known about local changes in bone structure compared to cartilage degeneration. This study linked structural adaptation of subchondral bone with histological OA grade. Twenty‐five osteochondral samples of macroscopically different degeneration were prepared from tibiae of 14 patients. Samples were scanned with micro‐computed tomography (μCT) and both conventional structural parameters and novel 3D parameters based on local patterns were analyzed from the subchondral plate and trabecular bone. Subsequently, samples were processed for histology and evaluated for OARSI grade. Each bone parameter and OARSI grade was compared to assess structural adaptation of bone with OA severity. In addition, thicknesses of cartilage, calcified cartilage, and subchondral plate were analyzed from histological sections and compared with subchondral bone plate thickness from μCT. With increasing OARSI grade, the subchondral plate became thicker along with decreased specific bone surface, while there was no change in tissue mineral density. Histological analysis showed that subchondral plate thickness from μCT also includes calcified cartilage. Entropy of local patterns increased with OA severity, reflecting higher tissue heterogeneity. In the trabecular compartment, bone volume fraction and both trabecular thickness and number increased with OARSI grade while trabecular separation and structure model index decreased. Also, elevation of local patterns became longitudinal in the subchondral plate and axial transverse in trabecular bone with increasing OARSI grade. This study demonstrates the possibility of radiological assessment of OA severity by structural analysis of bone. © 2016 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. J Orthop Res 35:785–792, 2017.