5 publicaciones etiquetados con "Psoriasis"

Publicado en JID Innovations, este estudio revisado por pares presenta el Generalized Pustular Psoriasis Physician Global Assessment automático (AGPPGA), una herramienta basada en inteligencia artificial que puntúa la pustulación, el eritema y la descamación a partir de imágenes clínicas de psoriasis pustulosa generalizada (PPG), una enfermedad rara y potencialmente mortal cuya gravedad es difícil de evaluar de forma homogénea.

Resumen​

• Tipo de estudio: Desarrollo y validación retrospectiva de una herramienta de puntuación de gravedad basada en IA
• Revista: JID Innovations (2026)
• DOI: https://doi.org/10.1016/j.xjidi.2026.100521
• Conjuntos de datos: 2 conjuntos de imágenes de PPG, V1 (332 imágenes) y V2 (4296 imágenes de 46 pacientes), con diversidad de sexos, edades y tonos de piel
• Estándar de referencia: consenso de 2 paneles de dermatólogos con certificación europea y experiencia en psoriasis y PPG (3 anotadores para V1 y 4 para V2)
• Objetivo: Evaluar automáticamente los 3 signos visuales del GPPGA (pústulas, eritema y descamación) y comparar la concordancia de la herramienta con el consenso de expertos frente a la de los propios dermatólogos

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.