← Capillary.io team publications
Publications by other researchers
Explore how researchers use Capillary.io and CAPI-Detect in their studies, and read expert perspectives on CAPI-Detect, CAPI-Score and automated capillaroscopy.
Research articles, clinical reports and letters describing research with Capillary.io or CAPI-Detect.
Stroke. 2026;57(10):e483–e484.
Examines the relationship between nailfold capillary abnormalities and cerebral small vessel disease burden in 289 older adults from the Atahualpa cohort in Ecuador.
The study’s preprint describes image analysis with Capillary.io. The research draws on the same Atahualpa cohort as the study of white matter hyperintensities.
Angiología. 2026;78(5):216–224.
A pilot case-control study of 20 patients with peripheral arterial disease and 20 healthy controls, using Capillary.io to quantify capillary density, diameters and morphology in fingers and toes. Patients showed lower capillary density in the fingers and larger arterial and venous capillary diameters in the toes.
Published on 23 September 2026.
Clinical and Translational Medicine. 2026;16(9):e70789.
University of Michigan researchers used Capillary.io to detect, classify and quantify nailfold capillary features in juvenile myositis and comparison groups.
ACR Open Rheumatology. 2026;8(8):e90106.
An Australian study of nicotinamide supplementation in primary and scleroderma-related Raynaud phenomenon, using Capillary.io to assess nailfold capillaroscopy images.
Journal of Primary Care & Community Health. 2026;17:21501319261473346.
A population study of 306 older adults in Ecuador examining oily fish intake and microvascular integrity. Capillary.io analyzed the images automatically, followed by expert review.
Arthritis & Rheumatology. 2026;78(7):1557–1558.
A single-patient report using CAPI-Detect on Capillary.io for serial capillaroscopy in anti–Mi-2 dermatomyositis. It describes microvascular improvement occurring later than clinical remission.
Journal of Stroke and Cerebrovascular Diseases. 2026;35:108633.
A study of 289 older adults in Ecuador combining nailfold capillaroscopy with brain MRI. Capillary.io quantified capillary abnormalities to help researchers examine their relationship with white matter hyperintensities.
Angiogenesis. 2026;29(3):47.
A clinical letter exploring possible microvascular vulnerability in carotid artery dissection. Automated quantitative analysis with CAPI-Detect identified enlarged nailfold capillaries.
Angiogenesis. 2026;29(2):14.
A clinical letter on microvascular abnormalities in vertebral hemangiomas. CAPI-Detect generated a quantitative report showing enlarged capillaries and microhemorrhages, with an overall non-specific pattern.
Seminars in Arthritis and Rheumatism. 2026;78:152961.
A study of 38 US veterans with Raynaud phenomenon comparing how clearly nailfold images can be interpreted across skin tones using a dermatoscope and a USB capillaroscope.
The earlier ACR 2025 conference abstract describes analysis with Capillary.io of capillary density, apical enlargement, hemorrhages and abnormal shapes.
Hormone Molecular Biology and Clinical Investigation. 2026;47(2):33–37.
A case report and narrative review of capillaroscopic abnormalities during dienogest treatment, using CAPI-Detect for automated quantitative analysis of capillary morphology.
Mediterranean Journal of Rheumatology. 2025;36(2).
A case report on navicular algodystrophy and microvascular abnormalities. Capillaroscopy was assessed using Capillary.io and its CAPI-Detect algorithm alongside expert interpretation.
ACR Open Rheumatology. 2025;7(1):e11784.
A case report using Capillary.io to assess a scleroderma-like capillaroscopic pattern in a patient without an underlying connective tissue disease.
A published correction updates an apical-diameter measurement in Figure 1.
Angiogenesis. 2025;28(2):22.
A clinical letter on microvascular and cerebrovascular abnormalities in Raynaud phenomenon and fibromyalgia, using CAPI-Detect to quantify enlarged capillaries.
Rheumatology International. 2025;45(9):203.
A case report and literature review describing an active scleroderma pattern within three months of Raynaud onset. CAPI-Detect generated quantitative measurements and classified the capillaroscopic pattern as active.
Conference abstracts from studies using or evaluating Capillary.io or CAPI-Detect.
EULAR 2026. Abstract POS0155.
Researchers in Varna, Bulgaria, compare automated capillaroscopy using Capillary.io with expert assessment in connective tissue diseases. The abstract reports preliminary diagnostic accuracy results.
ACR Convergence 2025. Abstract 2158. Arthritis & Rheumatology. 2025;77(suppl 9).
A UCSF and UC Berkeley study examining nailfold capillary changes in juvenile dermatomyositis. Capillary.io measured capillary limb widths and apical diameters, followed by manual review.
ACR Convergence 2025. Abstract 0252. Arthritis & Rheumatology. 2025;77(suppl 9).
A Colombian study of 136 patients with autoimmune rheumatic diseases. Images acquired with Smart G-Scope were processed using Capillary.io to characterize capillaroscopic patterns.
Research manuscripts shared as preprints before peer review.
SSRN. 2026. doi:10.2139/ssrn.7008573.
A study of 51 women with antiphospholipid syndrome using Capillary.io to assess capillaroscopic patterns and quantitative metrics, and their relationships with antibody profiles, vascular events and obstetric morbidity.
Expert perspectives on automated capillaroscopy, including editorials, reviews, book chapters and comparative studies that discuss or cite CAPI-Detect, CAPI-Score and related research.
International Journal of Rheumatic Diseases. 2026;29(10):e70902.
An independent editorial on Raynaud’s phenomenon cites CAPI-Detect when discussing the potential of AI-assisted capillaroscopy to improve reproducibility, reduce observer variability and shorten interpretation time.
The authors describe AI-assisted capillaroscopy as an emerging research tool requiring further external and prospective validation.
In: El Miedany Y, Elziaty RA, editors. Capillaroscopy. Contemporary Rheumatology. Springer. 2026:313–337.
A book chapter reviewing advances in digital capillaroscopy, quantitative analysis and artificial intelligence. It cites CAPI-Score, CAPI-Detect and earlier Capillary.io research on automated image analysis and its clinical validation.
Arthritis Care & Research. 2026. Published online. doi:10.1002/acr.80068.
A narrative review of AI in systemic sclerosis that discusses how CAPI-Detect uses machine learning to analyze capillary morphology and identify diagnostic variables. It also examines the need to validate automated microvascular assessment across centers and imaging devices.
Rheumatology (Bulgaria). 33(3):11–23. Published online 10 February 2026.
A review of AI-assisted capillaroscopy discussing Capillary.io, CAPI-Score and CAPI-Detect. It explores automated measurement and pattern classification alongside the continuing role of expert clinical interpretation.
Sclerosis. 2026;4(2):10.
A review of nailfold capillaroscopy for diagnosis, prognosis and monitoring in systemic sclerosis. It cites CAPI-Detect when discussing how automated image analysis could reduce observer variability, accelerate assessment and improve reproducibility.
Arthritis Care & Research. 2026;78(1):106–112.
A narrative review of Raynaud-related pain and treatment research. It cites CAPI-Detect and CAPI-Score when discussing accessible image acquisition and cloud-based capillaroscopy interpretation beyond specialist centers.
Rheumatology. 2026;65(2):keag081.
A review of digital vasculopathy assessment in systemic sclerosis, including quantitative and automated analysis of nailfold capillaroscopy images. It cites CAPI-Score among advances that may contribute to more personalized assessment and care.
Frontiers in Endocrinology. 2026;17:1866310.
A narrative review exploring nailfold capillaroscopy in diabetic retinopathy. It cites CAPI-Score and CAPI-Detect as methodological references for quantitative analysis and discusses how similar approaches might inform future research in diabetes.
The proposed diabetic-retinopathy framework is exploratory. Applying these approaches to diabetes requires disease-specific validation.
Canadian Rheumatology Today. 2025;2(1):5–15.
A clinical review linking capillaroscopy with diagnosis and risk stratification in systemic autoimmune rheumatic diseases. It cites CAPI-Detect when describing machine-learning software that helps identify abnormal capillaries, including changes associated with scleroderma.
In: Herrick AL, Murray A, Taylor C, Smith V, Cutolo M, editors. Nailfold Capillaroscopy in Rheumatological Diseases. Springer. 2025:65–89.
A book chapter on objective measurement and automated analysis in nailfold capillaroscopy. It reviews quantitative approaches and their remaining challenges, and includes both CAPI-Detect and CAPI-Score in its references.
First published online on 2 January 2026.
Applied Sciences. 2025;15(21):11666.
A narrative review of AI in rheumatology imaging and laboratory diagnostics. It discusses CAPI-Detect and CAPI-Score in a dedicated section on automated capillaroscopy, placing them alongside other approaches to image analysis and disease-pattern classification.
Clinical and Experimental Rheumatology. 2025;43(8):1499–1507.
A Brazilian study of 258 capillaroscopies evaluates selected criteria from CAPI-Score and Fast Track for identifying scleroderma patterns. Lower capillary-density thresholds showed high specificity but lower sensitivity; the authors propose a modified approach combining density and giant capillaries.
The comparison excludes abnormal capillary shapes and evaluates selected published criteria rather than the complete automated Capillary.io system.
Rheumato. 2025;5(4):17.
A review of AI and biomarkers in autoimmune rheumatic diseases that discusses CAPI-Detect within automated microvascular assessment. It examines the potential of these approaches for precision medicine alongside the need for further external validation and prospective clinical studies.
Rheumatology. 2024;63(12):3203–3204.
An independent editorial dedicated to the CAPI-Score paper. It discusses the algorithm’s potential to reduce observer variability, quantify capillary morphology and distinguish disease patterns, while acknowledging challenges in interpretation and access.