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In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. This systematic review aims to identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and identify gaps in current clinical applications of these tools. OVID Embase, OVID MEDLINE, ACM Digital Library, Scopus, and IEEE Xplore were searched from inception to August, 2024. A total of 30 articles met our inclusion criteria and were included in this review. One article used serum biomarkers, while the remaining 29 used image-based models. The most common severity assessment score employed by these ML models was the Psoriasis Area Severity Index score, followed by Body Surface Area, with fifteen and five articles, respectively. The small size and heterogeneity of the existing literature are the primary limitations of this review. Progress in assessing skin lesion severity through ML in dermatology has advanced, but prospective clinical applications remain limited. ML and AI promise to improve psoriasis management, especially in non-image-based applications requiring further exploration. Large-scale prospective trials using diverse image datasets are necessary to evaluate and predict the clinical value of these predictive AI models.
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BACKGROUND: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy. OBJECTIVE: This review aims to summarise the existing literature on how ML can be applied to CD in its entirety. METHODS: Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD. RESULTS: 7834 articles were identified in the search, with 110 moving to full-text review, and six articles included. Two used ML to identify key biomarkers to help distinguish between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD), three used image data to distinguish between ACD and ICD, and one used clinical and demographical data to predict the risk of positive patch tests. All studies used supervision in their ML model training with a total of 49 704 patients across all data sets. There was sparse reporting of the accuracy of these models. CONCLUSIONS: Although the available research is still limited, there is evidence to suggest that ML has potential to support diagnostic outcomes in a clinical setting. Further research on the use of ML in clinical practice is recommended.
Assuntos
Dermatite Alérgica de Contato , Dermatite Irritante , Aprendizado de Máquina , Testes do Emplastro , Humanos , Dermatite Alérgica de Contato/diagnóstico , Dermatite Alérgica de Contato/etiologia , Testes do Emplastro/métodos , Dermatite Irritante/diagnóstico , Dermatite Irritante/etiologia , Diagnóstico DiferencialRESUMO
Post inflammatory hyperpigmentation (PIH) affects all skin types with a heightened predilection for darker skin tones. Its course is chronic once developed and treatment is often difficult. This systematic review aims to summarize the treatment outcomes for PIH with a focus on skin of colour (SOC) individuals. A literature search was conducted using MEDLINE (from 1946), Embase (from 1974), PubMed, and Cochrane in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline. Results from 48 studies summarized 1356 SOC individuals. The mean age was 29 years (n = 1036) and 78% were female (n = 786). The ethnic prevalence was 70% Black, 27% Asian, and 3% Latin. Overall, 20% were Fitzpatrick skin type (FST) III, 40% FST IV, 34% FST V, and 6% FST VI. Most cases were precipitated by inflammatory conditions (89%) and localized to the face (83%). The most frequently reported interventions were topical retinoids (22%) and laser therapy (17%). Partial improvement was seen in 85% and 66% of participants, respectively. Laser was the only intervention that offered complete resolution in a subgroup of patients (26%); however, there were reported cases of PIH exacerbation following treatment. Chemical peels (9%) and hydroquinone (7%) were among other treatments with less effective outcomes. PIH and its persistence is a prevalent issue, significantly affecting many affected individuals with darker skin tones. Our results show a lack of robust efficacy across all treatment modalities. There is considerable room for improvement in interventions for at-risk populations.
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Previous studies indicated that ductal cells can contribute to endocrine neogenesis in adult rodents after alpha cells convert into beta cells. This can occur through Pax4 mis-expression in alpha cells or through long-term administration of gamma-aminobutyric acid (GABA) to healthy mice. GABA has also been reported to increase the number of beta cells through direct effects on their proliferation, but only in specific genetic mouse backgrounds. To test whether GABA induces neogenesis of beta cells from ductal cells or affects pancreatic cell proliferation, we administered GABA or saline over 2 or 6 months to Sox9CreER;R26RYFP mice in which 60-80% of large or small ducts were efficiently lineage labeled. We did not observe any increases in islet neogenesis from ductal cells between 1 and 2 months of age in saline treated mice, nor between 2 and 6 months of saline treatment, supporting previous studies indicating that adult ductal cells do not give rise to new endocrine cells during homeostasis. Unlike previous reports, we did not observe an increase in beta cell neogenesis after 2 or 6 months of GABA administration. Nor did we observe a significant increase in the pancreatic islet area, the number of insulin and glucagon double positive cells, or cell proliferation in the pancreas. This indicates that the effect of long term GABA administration on the pancreas is minimal or highly context dependent.
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Células Endócrinas , Células Secretoras de Glucagon , Células Secretoras de Insulina , Ilhotas Pancreáticas , Camundongos , Animais , Ductos Pancreáticos , Ácido gama-Aminobutírico/farmacologiaRESUMO
BACKGROUND: Substantial changes in abortion care regulations, available medications and national clinical practice guidelines have occurred since a 2012 national Canadian Abortion Provider Survey (CAPS). We developed and piloted the CAPS 2019 survey instrument to explore changes of the abortion provider workforce, their clinical care as well as experiences with stigma and harassment. METHODS: We undertook development and piloting in three phases: (1) development of the preliminary survey sections and questions based on the 2012 survey instrument, (2) content validation and feasibility of including certain content aspects via a modified Delphi Method with panels of clinical and research experts, and (3) pilot testing of the draft survey for face validity and clarity of language; assessing usability of the web-based Research Electronic Data Capture platform including the feasibility of complex skip pattern functionality. We performed content analysis of phase 2 results and used a general inductive approach to identify necessary survey modifications. RESULTS: In phase 1, we generated a survey draft that reflected the changes in Canadian abortion care regulations and guidelines and included questions for clinicians and administrators providing first and second trimester surgical and medical abortion. In phase 2, we held 6 expert panel meetings of 5-8 participants each representing clinicians, administrators and researchers to provide feedback on the initial survey draft. Due to the complexity of certain identified aspects, such as interdisciplinary collaboration and interprovincial care delivery differences, we revised the survey sections through an iterative process of meetings and revisions until we reached consensus on constructs and questions to include versus exclude for not being feasible. In phase 3, we made minor revisions based on pilot testing of the bilingual, web-based survey among additional experts chosen to be widely representative of the study population. Demonstrating its feasibility, we included complex branching and skip pattern logic so each respondent only viewed applicable questions based on their prior responses. CONCLUSIONS: We developed and piloted the CAPS 2019 survey instrument suitable to explore characteristics of a complex multidisciplinary workforce, their care and experience with stigma on a national level, and that can be adapted to other countries.