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ArticleArthritis research & therapy2025

Artificial intelligence-based Raynaud's quantification index (ARTIX): an objective mobile-based tool for patient-centered assessment of Raynaud's phenomenon.

Marco Di Battista, Seda Colak, Anna Howard, Francesca Donadoni, Chris Owen-Smith, Andrea Rindone, Stefano Di Donato, Collette Hartley, Lesley-Anne Bissell, Francesco Del Galdo

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Article in Arthritis research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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10 authors.

Marco Di BattistaLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Seda ColakLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Anna HowardNIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals Trust, Leeds, UK.
Francesca DonadoniProcedure Health Limited, London, UK.
Chris Owen-SmithProcedure Health Limited, London, UK.
Andrea RindoneLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Stefano Di DonatoLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Collette HartleyLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Lesley-Anne BissellLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK.
Francesco Del GaldoLeeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, LS7 4SA, UK. f.delgaldo@leeds.ac.uk.

Funding

Leeds Biomedical Research Centre NIHR203331Versus Arthritis Translational Award (Grant Code 22413)
6 · The paper itself

Abstract

backgroundWe aimed to develop an artificial intelligence algorithm able to assess Raynaud's phenomenon (RP) from mobile phone photography, ensuring as a patient-centered, image-based method for RP quantification.

methodsARTIX (artificial intelligence-based Raynaud's quantification index) score was developed as a multi-step process of segmentation, decomposition and filters application to mobile phone pictures of the hand. ARTIX was validated by the ability to assess finger response to standardised cold challenge in patients with primary and secondary RP and healthy controls (HC) and compared with thermography as a reference.

resultsForty-five RP patients (91.1% female, mean age 52.2 years, 75.5% secondary RP) were enrolled, along with 22 HC comparable for age and gender. RP patients presented significantly lower ARTIX values than HC both at baseline (p < 0.001) and across all timepoints of the cold challenge (p < 0.01 for all), paralleling a similarly significant difference observed by thermography. ARTIX score was higher in males and in patients taking vasoactive drugs, whereas lower values were obtained in patients with late capillaroscopic pattern, diffuse cutaneous skin subset, or negative for anti-centromere antibodies. ARTIX showed also good ability to discriminate between RP and HC response to cold challenge.

conclusionWe developed and validated ARTIX, a novel machine learning-driven method for the objective quantification of RP. Real-life longitudinal studies in patients with RP will determine the value of ARTIX to complement patient self-assessment surrogate measures of RP activity and severity.

Indexed as

Artificial IntelligenceMobile ApplicationsRaynaud DiseaseAdultAgedAlgorithmsCell PhoneFemaleHumansMaleMiddle AgedPatient-Centered CarePhotographySeverity of Illness IndexThermographyArtificial intelligenceCold challengeRaynaud’s phenomenonSystemic sclerosisThermography

Identifiers

PMID40462205
PMCPMC12131432

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