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. 2025 Jun 3;27(1):120.
doi: 10.1186/s13075-025-03569-w.

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

Marco Di Battista  1   2 , Seda Colak  1   3 , Anna Howard  4 , Francesca Donadoni  5 , Chris Owen-Smith  5 , Andrea Rindone  1   6 , Stefano Di Donato  1   4 , Collette Hartley  1   4 , Lesley-Anne Bissell  1   4 , Francesco Del Galdo  7   8
Affiliations

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

Marco Di Battista et al. Arthritis Res Ther. .

Abstract

Background: We 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.

Methods: ARTIX (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.

Results: Forty-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.

Conclusion: We 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.

Keywords: Artificial intelligence; Cold challenge; Raynaud’s phenomenon; Systemic sclerosis; Thermography.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was conducted as part of the observational study STRIKE, approved by North East - Newcastle & North Tyneside 2 Research Ethics Committee (IRAS ID: 178638; REC Reference: 15/NE/0211). All participants voluntarily agreed to participate providing written informed consent. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Preliminary steps for ARTIX development
Fig. 2
Fig. 2
Density plots showing comprehensive differences (mean highlighted) between RP and HC results both for ARTIX and thermography regardless of seasonal variability
Fig. 3
Fig. 3
Mean trends during cold challenge for ARTIX and thermography showing constant differences between RP and HC regardless of seasonal variability. * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001
Fig. 4
Fig. 4
Differences between ARTIX and thermography in the proportions of subjects who returned to baseline values at the 10th minute of cold challenge
Fig. 5
Fig. 5
Significant clinical associations for ARTIX score in patients with secondary RP (summary statistics shown by box plots and data distribution by violin plots)
See this image and copyright information in PMC

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