Automatic Urticaria Activity Score: Deep Learning-Based Automatic Hive Counting for Urticaria Severity Assessment.

Chronic urticaria is a chronic skin disease that affects up to 1% of the general population worldwide, with chronic spontaneous urticaria accounting for more than two-thirds of all chronic urticaria cases. The Urticaria Activity Score (UAS) is a dynamic severity assessment tool that can be incorporated into daily clinical practice, as well as clinical trials for treatments. The UAS helps in measuring disease severity and guiding the therapeutic strategy. However, UAS assessment is a time-consuming and manual process, with high interobserver variability and high dependence on the observer. To tackle this issue, we introduce Automatic UAS, an automatic equivalent of UAS that deploys a deep learning, lesion-detecting model called Legit.Health-UAS-HiveNet. Our results show that our model assesses the severity of chronic urticaria cases with a performance comparable to that of expert physicians. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new end point in clinical trials. This proves the usefulness of artificial intelligence in the practice of evidence-based medicine; models trained on the consensus of large clinical boards have the potential of empowering clinicians in their daily practice and replacing current standard clinical end points in clinical trials.

Grado de conocimientos sobre el riesgo de conducta suicida de los profesionales de Atención Primaria en un área de salud / [Degree of knowledge about the risk of suicidal behavior of Primary Care professionals in a health area]

Introducción: En los últimos años ha aumentado el número de suicidios en nuestro país con 4003 suicidios en 2021 (el mayor número desde que existen datos), representando también la 1ª causa de muerte externa. A su vez un 77% de las personas que se suicidan tuvieron contacto con algún profesional de atención primaria en el año previo y un 45% el mes previo al suicidio. Material y método: Se empleó un formulario autoadministrado de Google para recoger información acerca del conocimiento de la conducta e ideación suicida, los factores de riesgo y las acciones que se llevan a cabo ante esta situación en Atención Primaria por parte de los profesionales de Enfermería y Medicina. Resultados: A través de la encuesta autoadministrada, se concluyó que casi la totalidad de los participantes están de acuerdo en la relevancia del aumento del número de suicidios en 2022 en España. Más del 50% de la muestra afirma tener un conocimiento escaso o ningún conocimiento acerca de la conducta de riesgo suicida, su detección y abordaje desde Atención Primaria. Además, más del 75% indica no haber recibido formación sobre la prevención de la ideación suicida, considerando necesaria una preparación específica para el adecuado abordaje en la prevención y atención del riesgo de suicidio. Conclusiones: La presente investigación identifica la necesidad de una mayor formación de los profesionales de Atención Primaria en el área de salud estudiada, para realizar un correcto abordaje del paciente suicida. (AU)

Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4): A novel tool to assess the severity of hidradenitis suppurativa using artificial intelligence.

BACKGROUND: Hidradenitis suppurativa (HS) is a painful chronic inflammatory skin disease that affects up to 4% of the European adult population. International Hidradenitis Suppurativa Severity Score System (IHS4) is a dynamic scoring tool that was developed to be incorporated into the doctor's daily clinical practice and clinical studies. This helps measure disease severity and guides the therapeutic strategy. However, IHS4 assessment is a time-consuming and manual process, with high inter-observer variability and high dependence on the observer's expertise. MATERIALS AND METHODS: We introduce the Automatic International Hidradenitis Suppurativa Severity Score System (AIHS4), an automatic equivalent of IHS4 that deploys a deep learning model for lesion detection, called Legit.Health-IHS4net, based on the YOLOv5 architecture. AIHS4 was trained on Legit.Health-HS-IHS4, a collection of HS images manually annotated by six specialists and processed by a novel knowledge unification algorithm. RESULTS: Our results show that, with the current dataset size, our tool assesses the severity of HS cases with a performance comparable to that of the most expert physician. Furthermore, the model can be implemented into CADx systems to support doctors in their clinical practice and act as a new endpoint in clinical trials. CONCLUSION: Our work proves the potential usefulness of artificial intelligence in the practice of evidence-based dermatology: models trained on the consensus of large clinical boards have the potential to empower dermatologists in their daily practice and replace current standard clinical endpoints.

Sonography of subcutaneous nodules following immunization with histopathological correlation: a three-case series.

Subcutaneous nodules are a rare adverse event following immunization frequently associated with suboptimal injection procedures and aluminium-containing vaccines. We present three cases of subcutaneous nodules following immunization describing their clinical signs, histopathological features and ultrasound findings and demonstrating the use of sonography as an aid to the diagnosis of this entity.

Penfigoide ampolloso e inhibidores de la DPP4 / Bullous pemphigoid and DPP4 inhibitors

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