Automatic assessment of nailfold capillaroscopy software: a pilot study

Introduction Capillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed...

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Main Authors: Olga Elżbieta Brzezińska, Krzysztof Andrzej Rychlicki-Kicior, Joanna Samanta Makowska
Format: Article
Language:English
Published: Termedia Publishing House 2024-11-01
Series:Rheumatology
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Online Access:https://reu.termedia.pl/Automatic-assessment-of-nailfold-capillaroscopy-software-a-pilot-study,194040,0,2.html
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author Olga Elżbieta Brzezińska
Krzysztof Andrzej Rychlicki-Kicior
Joanna Samanta Makowska
author_facet Olga Elżbieta Brzezińska
Krzysztof Andrzej Rychlicki-Kicior
Joanna Samanta Makowska
author_sort Olga Elżbieta Brzezińska
collection DOAJ
description Introduction Capillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capil­lary counting and image classification as normal or pathological. Material and methods The study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed. Results The results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%. Conclusions The pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.
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institution Kabale University
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2084-9834
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spelling doaj-art-47a54c1dc4624f2a865f1bb6b1dad9a72025-01-27T11:20:37ZengTermedia Publishing HouseRheumatology0034-62332084-98342024-11-0162534635010.5114/reum/194040194040Automatic assessment of nailfold capillaroscopy software: a pilot studyOlga Elżbieta Brzezińska0https://orcid.org/0000-0002-3885-8497Krzysztof Andrzej Rychlicki-Kicior1https://orcid.org/0000-0002-3419-745XJoanna Samanta Makowska2https://orcid.org/0000-0003-2036-375XDepartment of Rheumatology, Medical University of Lodz, PolandUniversity of Economics and Human Sciences in Warsaw, PolandDepartment of Rheumatology, Medical University of Lodz, PolandIntroduction Capillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capil­lary counting and image classification as normal or pathological. Material and methods The study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed. Results The results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%. Conclusions The pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.https://reu.termedia.pl/Automatic-assessment-of-nailfold-capillaroscopy-software-a-pilot-study,194040,0,2.htmlmicroangiopathycapillaroscopydeep learningartificial intelligence
spellingShingle Olga Elżbieta Brzezińska
Krzysztof Andrzej Rychlicki-Kicior
Joanna Samanta Makowska
Automatic assessment of nailfold capillaroscopy software: a pilot study
Rheumatology
microangiopathy
capillaroscopy
deep learning
artificial intelligence
title Automatic assessment of nailfold capillaroscopy software: a pilot study
title_full Automatic assessment of nailfold capillaroscopy software: a pilot study
title_fullStr Automatic assessment of nailfold capillaroscopy software: a pilot study
title_full_unstemmed Automatic assessment of nailfold capillaroscopy software: a pilot study
title_short Automatic assessment of nailfold capillaroscopy software: a pilot study
title_sort automatic assessment of nailfold capillaroscopy software a pilot study
topic microangiopathy
capillaroscopy
deep learning
artificial intelligence
url https://reu.termedia.pl/Automatic-assessment-of-nailfold-capillaroscopy-software-a-pilot-study,194040,0,2.html
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