External clinical validation of automated software to identify structural abnormalities and microhaemorrhages in nailfold videocapillaroscopy images.

Data de publicació: 04/05/2023 Data Ahead of Print: 04/05/2023

Autors de IIS La Fe

• José Antonio Todolí Parra

  Autor

Autors aliens a IIS La Fe

• Gracia Tello, Borja C
• Ramos Ibanez, Eduardo
• Saez Comet, Luis
• Guillen Del Castillo, Alfredo
• Simeon Aznar, Carmen Pilar
• Selva O'Callaghan, Albert
• Espinosa, G.
• Lledo, G.
• Freire Dapena, Mayka
• Martinez Robles, Elena
• Rios, J. J.
• Mari Alfonso, Begona
• Ortego Centeno, Norberto
• Marin Ballve, Adela
• Callejas Rubio, Jose Luis
• Fonollosa Pla, Vicent
• Fanlo, P.

Grups d'Investigació

• Investigacion clínica en enfermedades crónicas e Infección por el VIH

  Investigador establecido

  Marta Montero Alonso

Abstract

OBJECTIVES: Automated 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. METHODS: A 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. CONCLUSIONS: This 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.

Keywords

• nailfold videocapillaroscopy; deep learning; machine learning; systemic sclerosis; Raynaud's phenomenon; algorithm; quantitative