External clinical validation of automated software to identify structural abnormalities and microhaemorrhages in nailfold videocapillaroscopy images
ObjectiveAutomated systems to analyse nailfold videocapillaroscopy (NVC) images are needed to promptly and comprehensively characterise patients with systemic sclerosis (SSc) or Raynaud's phenomenon (RP). We previously developed, and validated in-house, a deep convolutional neural network-based...
| Autores: | , , , , , , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Institut d'Investigació i Innovació Parc Taulí (I3PT) |
| Repositorio: | r-I3PT. Repositorio Institucional Producción Científica del Institut d'Investigació i Innovació Parc Taulí |
| OAI Identifier: | oai:i3pt.fundanetsuite.com:p4201 |
| Acceso en línea: | https://i3pt.portalinvestigacion.com/publicaciones/4201 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85166442714&doi=10.55563%2fclinexprheumatol%2fm6obl3&partnerID=40&md5=d53e263cb6aa8c1a4ad0b4a81364f3e7 |
| Access Level: | acceso abierto |
| Palabra clave: | nailfold videocapillaroscopy deep learning machine learning systemic sclerosis Raynaud's phenomenon algorithm quantitative |
| Sumario: | ObjectiveAutomated systems to analyse nailfold videocapillaroscopy (NVC) images are needed to promptly and comprehensively characterise patients with systemic sclerosis (SSc) or Raynaud's phenomenon (RP). We previously developed, and validated in-house, a deep convolutional neural network-based algorithm to classify NVC-captured images according to the presence/absence of structural abnormalities and/or microhaemorrhages. We present its external clinical validation.MethodsA total of 1,164 NVC images of RP patients were annotated by 5 trained capillaroscopists according to the following categories: normal capillary; dilation; giant capillary; abnormal shape; tortuosity; microhaemorrhage. The images were also presented to the algorithm. Matches and discrepancies between algorithm predictions and those annotations obtained by consensus of = 3 or = 4 interobservers were analysed.Results Consensus among = 3 capillaroscopists was achieved in 86.9% of images, 75.8% of which were correctly predicted by the algorithm. Consensus among = 4 experts occurred in 52.0% of cases, in which 87.1% of the algorithm's results matched with those of the expert panel. The algorithm's positive predictive value was >80% for microhaemorrhages and unaltered, giant or abnormal capillaries. Sensitivity was >75% for dilations and tortuosities. Negative predictive value and specificity were >89% for all categories.ConclusionThis external clinical validation suggests that this algorithm is useful to assist in the diagnosis and follow-up of SSc or RP patients in a timely manner. It may also be helpful in the management of patients with any pathology presenting with microvascular changes, as the algorithm has been designed to also be useful for research aiming at extending the usage of nailfold capillaroscopy to more conditions. |
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