The challenge of comprehensive nailfold videocapillaroscopy practice: a further contribution

Tello, B. Gracia; Ibañez, Eduardo Ramos; Mateo, Patricia Fanlo; Cómet, Luis Sáez; Robles, E. Martínez; Blanco, Juan José Ríos; Alfonso, Begoña Marí; Garriga, Gerard Espinosa; Parra, José Todolí; Centeno, Norberto Ortego; Rubio, José Luis Callejas; Dapena, Mayka Freire; Ballvé, Adela Marín; Selva-O’Callaghan, Albert; Castillo, Alfredo Guillén del; Simeón-Aznar, Carmen Pilar; Fonollosa, Vicent

doi:10.55563/clinexprheumatol/6usce8

Cited by 11 publications

(26 citation statements)

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“…The system is able to process images and to measure capillary density, mean capillary width, maximum capillary width, shape and derangement of capillaries in 1–2 min per finger [ 7 , 13–15 ]. A further contribution was made by Gracia Tello et al [ 16 ], who applied deep learning, i.e. Retinanet , to NFC image analysis, albeit with a smaller dataset than the current study.…”

Section: Introductionmentioning

confidence: 97%

Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model

et al. 2022

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Objectives The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an ‘off-the-shelf’ artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT’s analysis performance with that of practising rheumatologists. Methods NFC images of patients prospectively enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries were used. The primary outcome investigated was the ViT’s classification performance for identifying disease-associated changes (enlarged capillaries, giant capillaries, capillary loss, microhaemorrhages) and the presence of the scleroderma pattern in these images using a cross-fold validation setting. The secondary outcome involved a comparison of the ViT’s performance vs that of rheumatologists on a reliability set, consisting of a subset of 464 NFC images with majority vote–derived ground-truth labels. Results We analysed 17 126 NFC images derived from 234 EUSTAR and 55 VEDOSS patients. The ViT had good performance in identifying the various microangiopathic changes in capillaries by NFC [area under the curve (AUC) from 81.8% to 84.5%]. In the reliability set, the rheumatologists reached a higher average accuracy, as well as a better trade-off between sensitivity and specificity compared with the ViT. However, the annotators’ performance was variable, and one out of four rheumatologists showed equal or lower classification measures compared with the ViT. Conclusions The ViT is a modern, well-performing and readily available tool for assessing patterns of microangiopathy on NFC images, and it may assist rheumatologists in generating consistent and high-quality NFC reports; however, the final diagnosis of a scleroderma pattern in any individual case needs the judgement of an experienced observer.

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Section: Introductionmentioning

confidence: 97%

Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model

et al. 2022

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“…AUTOCAPI measures capillary density for a single field of view (preselected by the operator) [ 16 ]. Capillary.io [ 13 ] and a system developed by Swiss researchers [ 14 ] both detect vessels and signs of abnormality (e.g. giant capillaries, microhaemorrhages) from a single field of view (as opposed to a nailfold mosaic).…”

Section: Discussionmentioning

confidence: 99%

“…A key difference between our approach and those of Gracia Tello et al . [ 13 ] and Garaiman et al . [ 14 ] is that they detect the presence/absence of discrete phenomena, whereas we extract continuous measurements of vessel morphology, allowing more subtle signs of microvascular abnormality to be detected.…”

Section: Discussionmentioning

confidence: 99%

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A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images

Bharathi

Berks

Dinsdale³

et al. 2023

Rheumatology

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Objectives Nailfold capillaroscopy is key to timely diagnosis of systemic sclerosis (SSc), but is often not used in rheumatology clinics because the images are difficult to interpret. We aimed to develop and validate a fully automated image analysis system to fill this gap. Methods We mimicked the image interpretation strategies of SSc experts, using deep learning networks to detect each capillary in the distal row of vessels and make morphological measurements. We combined measurements from multiple fingers to give a subject-level probability of SSc. We trained the system using high-resolution images from 111 subjects (Group A) and tested on images from subjects not in the training set: 132 imaged at high-resolution (Group B); 66 imaged with a low-cost digital microscope (Group C). Roughly half of each group had confirmed SSc, half were healthy controls or had primary Raynaud’s phenomenon (‘normal’). We also estimated the performance of SSc experts. Results We compared automated SSc probabilities with the known clinical status of patients (SSc versus ‘normal’), generating receiver operating characteristic curves (ROCs). For Group B, the area under the ROC (AUC) was 97% [94% - 99%] (median [90% confidence interval]), with equal sensitivity/specificity 91% [86% - 95%]. For Group C, AUC was 95% [88% - 99%], with equal sensitivity/specificity 89% [82% - 95%]. SSc expert consensus achieved sensitivity 82%, specificity 73%. Conclusion Fully automated analysis using deep learning can achieve diagnostic performance at least as good as SSc experts, and is sufficiently robust to work with low-cost digital microscope images.

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“…Also, such tools will assist rheumatologists in generating consistent and high-quality NFC reports, especially in the diagnosis of systemic sclerosis [ 19 ]. In a study by Gracia Tello et al, a precision rate of 83% and a recall rate of 92.44% were achieved in the identification of capillaries in the nail fold video-capillaroscopic images [ 20 ]. Integrating AI into such software can help physicians make better decisions.…”

Section: Reviewmentioning

confidence: 99%

Decoding Applications of Artificial Intelligence in Rheumatology

Chinnadurai,

Mahadevan,

Navaneethakrishnan

et al. 2023

Cureus

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Artificial intelligence (AI) is not a newcomer in medicine. It has been employed for image analysis, disease diagnosis, drug discovery, and improving overall patient care. ChatGPT (Chat Generative Pre-trained Transformer, Inc., Delaware) has renewed interest and enthusiasm in artificial intelligence. Algorithms, machine learning, deep learning, and data analysis are some of the complex terminologies often encountered when health professionals try to learn AI. In this article, we try to review the practical applications of artificial intelligence in vernacular language in the fields of medicine and rheumatology in particular. From the standpoint of the everyday physician, we have endeavored to encapsulate the influence of AI on the cutting edge of medical practice and the potential revolutionary shift in the realm of rheumatology.

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The challenge of comprehensive nailfold videocapillaroscopy practice: a further contribution

Cited by 11 publications

References 0 publications

Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model

Vision transformer assisting rheumatologists in screening for capillaroscopy changes in systemic sclerosis: an artificial intelligence model

A deep learning system for quantitative assessment of microvascular abnormalities in nailfold capillary images

Decoding Applications of Artificial Intelligence in Rheumatology

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