8 artigos com a tag "Severity"

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 Skin Health and Disease (Oxford Academic), this peer-reviewed study introduces ALADIN (Acne Lesion And Density INdex), an artificial intelligence-driven tool that combines inflammatory lesion count and spatial density from facial images to produce reproducible, interpretable acne severity scores aligned with the Investigator Global Assessment (IGA) scale.

Published in Skin Health and Disease (Oxford Academic), this peer-reviewed study introduces a deep learning framework that accurately measures skin lesion surface areas from standard smartphone images — a critical step for objective severity scoring in conditions assessed with tools such as PASI and SCORAD.

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.

The Automatic Urticaria Activity Score (AUAS) system has been published in the Journal of Investigative Dermatology (JID) Innovations, showcasing our deep learning-based approach for urticaria severity assessment.

The AIHS4, our novel system for scoring Hidradenitis Suppurativa, is detailed in Skin Research and Technology. This study exemplifies our commitment to developing practical AI solutions for complex dermatological conditions.

Conclusions​

The study conducted to evaluate the clinical performance, efficacy, and safety of the device has yielded promising results. The comprehensive analysis of the CUS, Data Utility questionnaire, SUS, and Patient Satisfaction questionnaire has provided valuable insights into the tool's effectiveness in supporting dermatologists in their clinical practice.

The observed sample mean of 76.67 on the CUS suggests that the device has been positively received by the participating specialists. Noteworthy is the unanimous agreement on the ease of use and the high rating for optimizing time according to each patient's needs. It's also worth noting that, despite that the medical device was positively rated by the specialists, the goal of achieving a mean of 80.00 on the CUS was not reached. This result was due to the lower sample size of specialists who completed the questionnaire. In this way, an outlier, and due to the small sample size, impacted disproportionately the overall result, especially for questions 7, 8, 9 and 11. As can be seen by the higher standard deviation and lower mean average in these specific questions. We need to take this fact into account for the following studies and implement measures to mitigate this effect, such as a larger sample size, which could have diluted the effect of the outliers over the statistical outcomes, or predefined management for outliers.

Additionally, the device demonstrated efficiency in generating reports, receiving high ratings from the specialists. These outcomes affirm the device's potential to streamline clinical workflows and enhance patient care.

The Data Utility questionnaire revealed unanimous agreement among specialists regarding the usefulness of a device to facilitate their regular practice. Moreover, the majority expressed a preference for utilizing a device to identify the severity of cases, indicating its potential as an aid in diagnostic support.

The System Usability Scale assessment further underlines the positive reception of the device. Specialists found the tool to be user-friendly, with high scores indicating ease of navigation and minimal complexity. The unanimous agreement on the ease of use and the absence of perceived expertise required to navigate the device emphasize its accessibility and suitability for clinicians.

Patient satisfaction is a crucial aspect of any medical tool or platform. The results of the Patient Satisfaction questionnaire indicate a generally positive response from patients. They found the device to be easy to use, useful in monitoring their condition, and were satisfied with the care provided through the device.

In conclusion, the device has demonstrated notable clinical utility, usability, and safety in the evaluation of dermatological pathologies. The positive responses from both specialists and patients affirm its potential to serve as a valuable clinical decision-support tool. Further research and real-world application are warranted to explore the device's broader impact on dermatological practice and patient care.

Summary​

• Code: LEGIT_COVIDX_EVCDAO_2022
• Status: Finished
• Start date: March 3rd, 2022
• Finish date: October 23rd, 2023
• Acceptance criteria:
  • A score of 8 or higher in the Clinical Utility Score (CUS) filled by the medical staff

Our ASCORAD (Automatic SCORing of Atopic Dermatitis) study, a collaboration with Dr. Ramon Grimalt, was published in the Journal of Investigative Dermatology (JID) Innovations. This study details our approach to automating severity assessment of atopic dermatitis and eczema.