5 artigos com a tag "Psoriasis"

Publicado na JID Innovations, este estudo revisado por pares apresenta o Generalized Pustular Psoriasis Physician Global Assessment automático (AGPPGA), uma ferramenta baseada em inteligência artificial que pontua a pustulação, o eritema e a descamação a partir de imagens clínicas de psoríase pustulosa generalizada (PPG), uma doença rara e potencialmente fatal cuja gravidade é difícil de avaliar de forma consistente.

Resumo​

• Tipo de estudo: Desenvolvimento e validação retrospectiva de uma ferramenta de pontuação de gravidade baseada em IA
• Revista: JID Innovations (2026)
• DOI: https://doi.org/10.1016/j.xjidi.2026.100521
• Conjuntos de dados: 2 conjuntos de imagens de PPG, V1 (332 imagens) e V2 (4296 imagens de 46 pacientes), com diversidade de sexos, idades e tons de pele
• Padrão de referência: consenso de 2 painéis de dermatologistas com certificação europeia e experiência em psoríase e PPG (3 anotadores para V1 e 4 para V2)
• Objetivo: Avaliar automaticamente os 3 sinais visuais do GPPGA (pústulas, eritema e descamação) e comparar a concordância da ferramenta com o consenso de especialistas com a dos próprios dermatologistas

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.