Detection and Grading of Radiographic Hand Osteoarthritis Using an Automated Machine Learning Platform.

OBJECTIVE: Automated machine learning (autoML) platforms allow health care professionals to play an active role in the development of machine learning (ML) algorithms according to scientific or clinical needs. The aim of this study was to develop and evaluate such a model for automated detection and grading of distal hand osteoarthritis (OA). METHODS: A total of 13,690 hand radiographs from 2,863 patients within the Swiss Cohort of Quality Management (SCQM) and an external control data set of 346 non-SCQM patients were collected and scored for distal interphalangeal OA (DIP-OA) using the modified Kellgren/Lawrence (K/L) score. Giotto (Learn to Forecast [L2F]) was used as an autoML platform for training two convolutional neural networks for DIP joint extraction and subsequent classification according to the K/L scores. A total of 48,892 DIP joints were extracted and then used to train the classification model. Heatmaps were generated independently of the platform. User experience of a web application as a provisional user interface was investigated by rheumatologists and radiologists. RESULTS: The sensitivity and specificity of this model for detecting DIP-OA were 79% and 86%, respectively. The accuracy for grading the correct K/L score was 75%, with a κ score of 0.76. The accuracy per DIP-OA class differed, with 86% for no OA (defined as K/L scores 0 and 1), 71% for a K/L score of 2, 46% for a K/L score of 3, and 67% for a K/L score of 4. Similar values were obtained in an independent external test set. Qualitative and quantitative user experience testing of the web application revealed a moderate to high demand for automated DIP-OA scoring among rheumatologists. Conversely, radiologists expressed a low demand, except for the use of heatmaps. CONCLUSION: AutoML platforms are an opportunity to develop clinical end-to-end ML algorithms. Here, automated radiographic DIP-OA detection is both feasible and usable, whereas grading among individual K/L scores (eg, for clinical trials) remains challenging.

Dorsal Finger Fold Recognition by Convolutional Neural Networks for the Detection and Monitoring of Joint Swelling in Patients with Rheumatoid Arthritis.

Digital biomarkers such as wearables are of increasing interest in monitoring rheumatic diseases, but they usually lack disease specificity. In this study, we apply convolutional neural networks (CNN) to real-world hand photographs in order to automatically detect, extract, and analyse dorsal finger fold lines as a correlate of proximal interphalangeal (PIP) joint swelling in patients with rheumatoid arthritis (RA). Hand photographs of RA patients were taken by a smartphone camera in a standardized manner. Overall, 190 PIP joints were categorized as either swollen or not swollen based on clinical judgement and ultrasound. Images were automatically preprocessed by cropping PIP joints and extracting dorsal finger folds. Subsequently, metrical analysis of dorsal finger folds was performed, and a CNN was trained to classify the dorsal finger lines into swollen versus non-swollen joints. Representative horizontal finger folds were also quantified in a subset of patients before and after resolution of PIP swelling and in patients with disease flares. In swollen joints, the number of automatically extracted deep skinfold imprints was significantly reduced compared to non-swollen joints (1.3, SD 0.8 vs. 3.3, SD 0.49, p < 0.01). The joint diameter/deep skinfold length ratio was significantly higher in swollen (4.1, SD 1.4) versus non-swollen joints (2.1, SD 0.6, p < 0.01). The CNN model successfully differentiated swollen from non-swollen joints based on finger fold patterns with a validation accuracy of 0.84, a sensitivity of 88%, and a specificity of 75%. A heatmap of the original images obtained by an extraction algorithm confirmed finger folds as the region of interest for correct classification. After significant response to disease-modifying antirheumatic drug ± corticosteroid therapy, longitudinal metrical analysis of eight representative deep finger folds showed a decrease in the mean diameter/finger fold length (finger fold index, FFI) from 3.03 (SD 0.68) to 2.08 (SD 0.57). Conversely, the FFI increased in patients with disease flares. In conclusion, automated preprocessing and the application of CNN algorithms in combination with longitudinal metrical analysis of dorsal finger fold patterns extracted from real-world hand photos might serve as a digital biomarker in RA.

[Adenosine deaminase 2 deficiency: a disease with multiple presentations]. / Déficit en adénosine désaminase 2 : une maladie aux présentations multiples.

Adenosine deaminase 2 deficiency (DADA2) is a genetic auto- inflammatory disease that most often presents in childhood, but that can also have a late onset in adulthood. It is characterized by vasculitis, mainly of the skin and nervous system most often in the form of a stroke, associated to immunodeficiency and cytopenias. The diagnosis is made by measuring adenosine deaminase 2 (ADA2) enzymatic activity and confirming the presence of mutations in the ADA2 gene by genetic testing. The treatment of choice for the inflammatory phenotype is the early administration of anti-TNFa to avoid the risk of major neurological disabilities. In the case of severe hematological involvement, hematopoietic stem cell transplantation is the only curative treatment currently available.

Le déficit en adénosine désaminase 2 (DADA2) est une maladie génétique auto-inflammatoire qui se manifeste le plus souvent à l'âge pédiatrique mais qui peut également débuter à l'âge adulte. Il se caractérise par une atteinte vasculitique responsable d'altérations cutanées et d'AVC associée à une immunodéficience et des cytopénies. Le diagnostic de DADA2 est posé par le dosage de l'activité de l'adénosine désaminase 2 (ADA2) et la confirmation par un test génétique d'une mutation dans le gène ADA2. Le traitement de choix du phénotype inflammatoire repose sur l'administration précoce d'anti-TNFα pour éviter la survenue d'un handicap neurologique majeur. En cas d'atteinte hématologique sévère, la greffe de cellules souches hématopoïétiques est le seul traitement curatif actuellement disponible.

[Medical students at the bedside of COVID-19 patients†: motivations and challenges]. / Étudiant·e·s en médecine engagé·e·s au chevet des patient·e·s hospitalisé·e·s pour COVID-19 - Motivations et enjeux.

The SARS-CoV-2 pandemic is putting our healthcare system under exceptional pressure, given the number of affected patients. In a context of limited human healthcare resources, senior medical students represent a valuable workforce that can quickly be mobilized for patient care. This is the approach followed in Switzerland and other countries, in several outpatient structures or inpatient services, including the Department of Internal Medicine, of the Lausanne University Hospital (CHUV). In this article, we first give the floor to students who responded to our call. We conclude with important considerations in terms of students' clinical supervision. It is reminded that the involvement of students in the care of COVID-19 patients should only occur on a vo luntary basis.

La pandémie de COVID-19 met notre système de santé sous une pression exceptionnelle, au vu du nombre de patient·e·s atteint·e·s. Dans un contexte de ressources humaines médico-soignantes limitées, les étudiant·e·s en médecine avancé·e·s dans leur cursus représentent un renfort très précieux, rapidement mobilisable auprès des patient·e·s. C'est la démarche suivie en Suisse et ailleurs dans le monde par diverses structures ambulatoires ou services hospitaliers, dont le Service de médecine interne du Centre hospitalier universitaire vaudois (CHUV). Dans cet article, nous donnons tout d'abord la parole aux étudiant·e·s qui ont répondu à notre appel. Nous terminons par des considérations importantes quant à l'accueil et l'accompagnement de ces étudiant·e·s. Il est rappelé que l'engagement d'étudiant·e·s auprès de patient·e·s souffrant de COVID-19 devrait se faire sur une base volontaire uniquement.