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

Effectiveness of progressive tendon-loading exercise therapy in patients with patellar tendinopathy:a randomised clinical trial

Objective To compare the effectiveness of progressive tendon-loading exercises (PTLE) with eccentric exercise therapy (EET) in patients with patellar tendinopathy (PT). Methods In a stratified, investigator-blinded, block-randomised trial, 76 patients with clinically diagnosed and ultrasound-confirmed PT were randomly assigned in a 1:1 ratio to receive either PTLE or EET. The primary end point was clinical outcome after 24 weeks following an intention-to-treat analysis, as assessed with the validated Victorian Institute of Sports Assessment for patellar tendons (VISA-P) questionnaire measuring pain, function and ability to play sports. Secondary outcomes included the return to sports rate, subjective patient satisfaction and exercise adherence. Results Patients were randomised between January 2017 and July 2019. The intention-to-treat population (mean age, 24 years, SD 4); 58 (76%) male) consisted of patients with mostly chronic PT (median symptom duration 2 years). Most patients (82%) underwent prior treatment for PT but failed to recover fully. 38 patients were randomised to the PTLE group and 38 patients to the EET group. The improvement in VISA-P score was significantly better for PTLE than for EET after 24 weeks (28 vs 18 points, adjusted mean between-group difference, 9 (95% CI 1 to 16); p=0.023). There was a trend towards a higher return to sports rate in the PTLE group (43% vs 27%, p=0.13). No significant between-group difference was found for subjective patient satisfaction (81% vs 83%, p=0.54) and exercise adherence between the PTLE group and EET group after 24 weeks (40% vs 49%, p=0.33). Conclusions In patients with PT, PTLE resulted in a significantly better clinical outcome after 24 weeks than EET. PTLE are superior to EET and are therefore recommended as initial conservative treatment for PT

A dens fracture case solved

Graphical Abstrac

A dens fracture case solved

Graphical Abstrac

Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography

BackgroundArtificial intelligence (AI) applications can facilitate detection of cervical spine fractures on CT and reduce time to diagnosis by prioritizing suspected cases.PurposeTo assess the effect on time to diagnose cervical spine fractures on CT and diagnostic accuracy of a commercially available AI application.Materials and methodsIn this study (June 2020 - March 2022) with historic controls and prospective evaluation, we evaluated regulatory-cleared AI-software to prioritize cervical spine fractures on CT. All patients underwent non-contrast CT of the cervical spine. The time between CT acquisition and the moment the scan was first opened (DNT) was compared between the retrospective and prospective cohorts. The reference standard for determining diagnostic accuracy was the radiology report created in routine clinical workflow and adjusted by a senior radiologist. Discrepant cases were reviewed and clinical relevance of missed fractures was determined.Results2973 (mean age, 55.4 ± 19.7 [standard deviation]; 1857 men) patients were analyzed by AI, including 2036 retrospective and 938 prospective cases. Overall prevalence of cervical spine fractures was 7.6 %. The DNT was 18 % (5 min) shorter in the prospective cohort. In scans positive for cervical spine fracture according to the reference standard, DNT was 46 % (16 min) shorter in the prospective cohort. Overall sensitivity of the AI application was 89.8 % (95 % CI: 84.2–94.0 %), specificity was 95.3 % (95 % CI: 94.2–96.2 %), and diagnostic accuracy was 94.8 % (95 % CI: 93.8–95.8 %). Negative predictive value was 99.1 % (95 % CI: 98.5–99.4 %) and positive predictive value was 63.0 % (95 % CI: 58.0–67.8 %). 22 fractures were missed by AI of which 5 required stabilizing therapy.ConclusionA time gain of 16 min to diagnosis for fractured cases was observed after introducing AI. Although AI-assisted workflow prioritization of cervical spine fractures on CT shows high diagnostic accuracy, clinically relevant cases were missed

Towards clinical implementation of an AI-algorithm for detection of cervical spine fractures on computed tomography

BackgroundArtificial intelligence (AI) applications can facilitate detection of cervical spine fractures on CT and reduce time to diagnosis by prioritizing suspected cases.PurposeTo assess the effect on time to diagnose cervical spine fractures on CT and diagnostic accuracy of a commercially available AI application.Materials and methodsIn this study (June 2020 - March 2022) with historic controls and prospective evaluation, we evaluated regulatory-cleared AI-software to prioritize cervical spine fractures on CT. All patients underwent non-contrast CT of the cervical spine. The time between CT acquisition and the moment the scan was first opened (DNT) was compared between the retrospective and prospective cohorts. The reference standard for determining diagnostic accuracy was the radiology report created in routine clinical workflow and adjusted by a senior radiologist. Discrepant cases were reviewed and clinical relevance of missed fractures was determined.Results2973 (mean age, 55.4 ± 19.7 [standard deviation]; 1857 men) patients were analyzed by AI, including 2036 retrospective and 938 prospective cases. Overall prevalence of cervical spine fractures was 7.6 %. The DNT was 18 % (5 min) shorter in the prospective cohort. In scans positive for cervical spine fracture according to the reference standard, DNT was 46 % (16 min) shorter in the prospective cohort. Overall sensitivity of the AI application was 89.8 % (95 % CI: 84.2–94.0 %), specificity was 95.3 % (95 % CI: 94.2–96.2 %), and diagnostic accuracy was 94.8 % (95 % CI: 93.8–95.8 %). Negative predictive value was 99.1 % (95 % CI: 98.5–99.4 %) and positive predictive value was 63.0 % (95 % CI: 58.0–67.8 %). 22 fractures were missed by AI of which 5 required stabilizing therapy.ConclusionA time gain of 16 min to diagnosis for fractured cases was observed after introducing AI. Although AI-assisted workflow prioritization of cervical spine fractures on CT shows high diagnostic accuracy, clinically relevant cases were missed

Association between clinical findings and the presence of lumbar spine osteoarthritis imaging features:A systematic review

OBJECTIVE: Spinal osteoarthritis is difficult to study and diagnose, partly due to the lack of agreed diagnostic criteria. This systematic review aims to give an overview of the associations between clinical and imaging findings suggestive of spinal osteoarthritis in patients with low back pain to make a step towards agreed diagnostic criteria.DESIGN: We searched MEDLINE, Embase, Web of Science, and CINAHL from inception to April 29, 2021 to identify observational studies in adults that assessed the association between selected clinical and imaging findings suggestive of spinal osteoarthritis. Risk of bias was assessed using the Newcastle Ottawa Scale and the quality of evidence was graded using an adaptation of the GRADE approach.RESULTS: After screening 7902 studies, 30 met the inclusion criteria. High-quality evidence was found for the longitudinal association between low back pain (LBP) intensity, and both disc space narrowing and osteophytes, as well as for the association between LBP-related physical functioning and lumbar disc degeneration, the presence of spinal morning stiffness and disc space narrowing and for the lack of association between physical functioning and Schmorl's nodes.CONCLUSIONS: There is high- and moderate-quality evidence of associations between clinical and imaging findings suggestive of spinal osteoarthritis. However, the majority of the studied outcomes had low or very low-quality of evidence. Furthermore, clinical and methodological heterogeneity was a serious limitation, adding to the need and importance of agreed criteria for spinal osteoarthritis, which should be the scope of future research.</p

ICON 2019: International Scientific Tendinopathy Symposium Consensus: Clinical Terminology

© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.Background Persistent tendon pain that impairs function has inconsistent medical terms that can influence choice of treatment.1 When a person is told they have tendinopathy by clinician A or tendinitis by clinician B, they might feel confused or be alarmed at receiving what they might perceive as two different diagnoses. This may lead to loss of confidence in their health professional and likely adds to uncertainty if they were to search for information about their condition. Clear and uniform terminology also assists inter-professional communication. Inconsistency in terminology for painful tendon disorders is a problem at numerous anatomical sites. Historically, the term ‘tendinitis’ was first used to describe tendon pain, thickening and impaired function (online supplementary figure S1). The term ‘tendinosis’ has also been used in a small number of publications, some of which were very influential.2 3 Subsequently, ‘tendinopathy’ emerged as the most common term for persistent tendon pain.4 5 To our knowledge, experts (clinicians and researchers) or patients have never engaged in a formal process to discuss the terminology we use. We believe that health professionals have not yet agreed on the appropriate terminology for painful tendon conditions.Peer reviewedFinal Accepted Versio