5 articles tagués avec « Psoriasis »

Publiée dans JID Innovations, cette étude évaluée par les pairs présente le Generalized Pustular Psoriasis Physician Global Assessment automatique (AGPPGA), un outil fondé sur l'intelligence artificielle qui évalue la pustulation, l'érythème et la desquamation à partir d'images cliniques de psoriasis pustuleux généralisé (PPG), une maladie rare et potentiellement mortelle dont la sévérité est difficile à évaluer de manière homogène.

Résumé​

• Type d'étude : Développement et validation rétrospective d'un outil d'évaluation de la sévérité fondé sur l'IA
• Revue : JID Innovations (2026)
• DOI : https://doi.org/10.1016/j.xjidi.2026.100521
• Jeux de données : 2 jeux d'images de PPG, V1 (332 images) et V2 (4296 images de 46 patients), avec une diversité de sexes, d'âges et de teintes de peau
• Référence : consensus de 2 panels de dermatologues certifiés en Europe, experts du psoriasis et du PPG (3 annotateurs pour V1 et 4 pour V2)
• Objectif : Évaluer automatiquement les 3 signes visuels du GPPGA (pustules, érythème et desquamation) et comparer la concordance de l'outil avec le consensus d'experts à celle des dermatologues eux-mêmes

Published in JMIR Dermatology, this peer-reviewed multireader multicase (MRMC) study evaluates how Legit.Health, an AI-based medical device, supports health care practitioners in identifying generalized pustular psoriasis (GPP), a rare and difficult-to-diagnose condition.

Summary​

• Study type: Multireader multicase (MRMC) study
• Journal: JMIR Dermatology (2026)
• DOI: https://doi.org/10.2196/82030
• Readers: 15 health care practitioners (11 primary care practitioners and 4 dermatologists)
• Cases: 100 images of GPP and visually similar conditions, reviewed first without device assistance and then with the device's top 5 predictions
• Objective: Assess whether the Legit.Health device improves the accuracy of health care practitioners in diagnosing generalized pustular psoriasis

The APASI, our innovative system for automated psoriasis severity assessment, is detailed in JEADV Clinical Practice. This study demonstrates our advancement in creating AI-powered solutions for precise dermatological evaluations.

Conclusions​

Legit.Health significantly enhanced primary care physicians' diagnostic accuracy, increasing it from 72.96% to 82.22%.

The impact of Legit.Health varied across different skin conditions, demonstrating significant improvements in hidradenitis suppurativa, urticaria, and actinic keratosis. However, p-values were not statistically significant for all cases, due to the low number of samples per pathology.

Approximately 49% of cases did not necessitate a referral. Additionally, 60.74% of cases across all specialities could be effectively managed remotely.

Reduction of referral and use of remote consultation​

Previous studies reported that 66% of patients visiting primary care HCPs are referred to dermatology, with very low (1%) remote consultation rates (González-López et al., 2019). In terms of urgent referral and triage, some institutions have reported that 76.8% of patients referred from primary HCPs to dermatology result in benign diagnoses (Pagani et al., 2023).

In this experiment, we found that 49% of cases should be referred according to the primary HCP with the information provided by the device, which is 17% lower than the aforementioned referral rates. Additionally, our results improve the remote consultation rates, suggesting that diagnostic support tools can help foster remote consultations.

Summary​

• Code: LEGIT.HEALTH_PH_2024
• Status: Finished
• Start date: June 4th, 2024
• Finish date: September 13th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy on both primary care physicians and dermatologists.
  • A reduction of 30% of referrals to dermatology (Warshaw et al. 2011).
  • An improvement in remote consultations.

Conclusions​

Legit.Health significantly enhanced primary care physicians' diagnostic accuracy, increasing it from 72.96% to 82.22%.

The impact of Legit.Health varied across different skin conditions, demonstrating significant improvements in hidradenitis suppurativa, urticaria, and actinic keratosis. However, p-values were not statistically significant for all cases, due to the low number of samples per pathology.

Approximately 49% of cases did not necessitate a referral. Additionally, 60.74% of cases across all specialities could be effectively managed remotely.

Reduction of referral and use of remote consultation​

Previous studies reported that 66% of patients visiting primary care HCPs are referred to dermatology, with very low (1%) remote consultation rates (González-López et al., 2019). In terms of urgent referral and triage, some institutions have reported that 76.8% of patients referred from primary HCPs to dermatology result in benign diagnoses (Pagani et al., 2023).

In this experiment, we found that 49% of cases should be referred according to the primary HCP with the information provided by the device, which is 17% lower than the aforementioned referral rates. Additionally, our results improve the remote consultation rates, suggesting that diagnostic support tools can help foster remote consultations.

Summary​

• Code: LEGIT.HEALTH_PH_2024
• Status: Finished
• Start date: June 24th, 2022
• Finish date: January 10th, 2024
• Acceptance criteria:
  • An improvement of diagnostic accuracy of 10% (Ferri et al. 2020) in primary care physicians and dermatologists.