Mahsa Babaei

Introduction: Migraine is a prevalent and disabling neurological disorder with subtypes mainly classified and managed based on monthly headache frequency. This study investigates a novel, data-driven clustering approach that uses comprehensive clinical features in addition to headache frequency to further understand migraine subtyping and management. Methods: This cross-sectional observational study included participants with a diagnosis of migraine based on self-reported surveys recruited from the Stanford Headache Clinic and surrounding community between 2012 and 2020. This study represents a secondary analysis of a previously collected cohort, incorporating newly added episodic migraine (EM) data alongside existing clinical and demographic variables. This approach extends prior work to refine data-driven migraine subtyping. Participants completed comprehensive surveys capturing clinical, demographic, and treatment-related information. A data-driven clustering approach was applied using hierarchical agglomerative clustering. To determine the optimal number of clusters, visual inspection of the dendrogram, scree plot, and principal component analysis was used. Descriptive statistics and logistic regression models were used to explore clinical characteristics and medication outcomes for each cluster. Results: 180 participants were included in the clustering analysis. Two distinct clusters were identified and compared. Cluster 1 ( n = 66; chronic migraine (CM) = 62; EM = 4) had a significantly ( p ≤ 0.001) higher migraine frequency, greater migraine-related disability, higher pain catastrophizing, and more comorbidities (including anxiety, depression, sleep disorders, post-traumatic stress disorder, and somatic symptoms). Cluster 2 ( n = 114; CM = 68; EM = 46) had significantly ( p ≤ 0.001) more years with headache and higher levels of pain self-efficacy. Treatment response differed significantly between clusters for acute medications, with Cluster 2 showing more favorable response rates to triptans and non-steroidal anti-inflammatory drugs ( p ≤ 0.001) and a higher proportion of individuals who indicated that acute medication helped to improve their migraine compared to Cluster 1. Conclusion: This study supports the potential use of a comprehensive phenotype-based clustering approach to determine migraine subtypes that correlate with acute treatment outcome.

Background: Menstrual-related migraine (MRM) is a subtype of migraine that significantly impacts women. Despite its high prevalence, MRM remains under-researched compared to other migraine types. This study investigates the phenotypic characteristics and treatment outcomes of MRM compared to non-menstrual migraine (non-MRM). Methods: This cross-sectional study included women aged 18 years or older with a diagnosis of migraine and an established menstruation pattern. Informed consent was properly obtained from all participants from the Stanford Headache Clinic and surrounding community between 2014 and 2024 prior to participation in the study. Data were collected from self-reported surveys, including demographic information, headache characteristics, physical and mental health status, and treatment outcomes from the Stanford Headache Clinic and surrounding community. Participants were then categorized into MRM and non-MRM groups. An exploratory analysis was performed to investigate phenotypic features, treatment outcomes, and brain volume through magnetic resonance imaging (MRI) study for each group. Statistical analyses included descriptive statistics, analysis of covariance (ANCOVA), and logistic regression. Results: A total of 84 subjects were included, with 38 in the MRM group and 46 in the non-MRM group. The mean age of participants was 34 (SD = 9) years. The MRM group (mean age 38 ± 9) showed significantly higher prevalences of recent change in migraine frequency, migraine attacks occurring in the same site, prodromal signs, speech aura, nausea, and vomiting associated with migraine, and a higher prevalence of reported family history of headache as compared with the non-MRM group (mean age 31 ± 10) (p < 0.05). MRM group also showed significantly higher rate of migraine improvement following the use of triptans and the overall acute and preventive medications (p < 0.05). No significant differences were found in brain volumes between the groups (p > 0.05). Conclusion: The MRM group was associated with significantly more bothersome symptoms associated with migraine, higher family history of headache, and more favorable response to overall acute medication especially triptan as compared with the non-MRM group.

Background: Acne‐induced scarring remains a formidable challenge, compelling patients to explore surgical interventions. This study aims to delve into and showcase recent strides in acne scar treatment, with a specific focus on innovative therapies. Methods: Conducting a narrative review spanning studies from 2013 to 2023, this paper provides a general overview of interventions. The review included English‐language studies with full‐text availability. Rigorous data screening and extraction by two independent authors covered diverse facets, including study particulars, participant profiles, measurement tools, intervention durations, outcomes, and key findings. The initial search was performed within PubMed, Scopus, Google Scholar, and the gray literature. Results: Ultimately, 26 studies meeting inclusion criteria were included and reviewed for final analysis. Conclusion: Recognizing the varied nature of acne scars, this narrative review underscores the need for personalized treatment strategies, considering scar type and severity. The study also emphasizes the potential necessity of combining treatments for optimal results. Despite substantial progress, ongoing research and more extensive studies are imperative to continually refine acne scar treatment outcomes, ultimately enhancing the well‐being of those affected by this condition.

Introduction: Teledermatology, a branch of telemedicine, utilizes telecommunication technologies for remote dermatological care, covering diagnosis, treatment planning, and patient monitoring. This study presents a review of the latest advances in teledermatology. Method: Database searches were performed within PubMed, EMBase, and Google Scholar using the keywords “Artificial Intelligence,” “Teledermatology,” “Telemedicine,” and “Dermatology.” All relevant documents were screened and included in the study. Results: Teledermatology employs synchronous and asynchronous interactions alongside remote monitoring, breaking geographical barriers to access dermatologic expertise. Supported by digital imaging, secure communication platforms, and artificial intelligence, teledermatology aids in diagnosis, treatment, and follow‐up. In education, teledermatology facilitates remote learning, case review, and interdisciplinary collaboration, providing access to diverse patient populations and mentorship opportunities. It also contributes to continuing medical education and telemedicine skill development. In research, teledermatology supports epidemiological studies, diagnostic accuracy assessments, clinical trials, and quality improvement initiatives. Despite its benefits, challenges like image quality, regulatory issues, and the digital divide persist. Conclusion: The evolution of teledermatology depends on technological advancements, regulatory frameworks, and reimbursement models. By addressing challenges and embracing innovation, teledermatology promises to enhance dermatologic care, patient outcomes, and healthcare efficiency in the digital era.

Introduction: Artificial intelligence (AI) in dermatology is both promising and challenging. This is important for clinicians to recognize most up‐to‐date knowledge and advancements.Results: Advantages include timely detection and diagnosis, enhanced accuracy, efficient triage, workflow automation, personalized treatment, outcome prediction, education support, telemedicine aid, research facilitation, cost‐efficiency, scalability, and drug development. However, limitations include generalizability issues, image quality variations, data requirements, interpretability concerns, patient acceptance, liability, multidisciplinary coordination challenges, data bias risks, lack of explainability, potential loss of clinical skills, security and privacy risks, resource intensiveness, integration challenges, validation and regulation gaps, and limited clinical assessment. Bridging these gaps requires robust ethical frameworks, interdisciplinary collaboration, education, regulation focusing on patient safety and privacy, standardized data sets, transparency, and human oversight.Conclusion: Despite the challenges, AI holds significant potential to revolutionize dermatology practice, improving patient outcomes, healthcare accessibility, and research advancements. Further research and interdisciplinary collaboration are crucial to maximize AI's benefits and address its limitations effectively.

Background: The integration of artificial intelligence (AI) in dermatology is revolutionizing the diagnostic methods and management strategies and hence is uplifting the overall patient care. AI technologies have shown a significant potential in automated diagnosis, severity assessment of chronic cutaneous diseases like psoriasis, and the development of comprehensive dermatological databases is helping in swift disease detection.Objective: This review aims to explore the current landscape and future potential of AI in inflammatory skin diseases. It focuses on various automated diagnostic systems, the role of AI in assessment and staging of chronic inflammatory dermatological conditions, and the importance of dermatological databases. The review also addresses the various challenges associated with AI implementation.Methods: A extensive literature search was conducted from databases namely, PubMed, Google Scholar, and Embase. Search terms included combinations of “artificial intelligence,” “deep learning,” “dermatology,” “automated diagnosis,” and “dermatological databases” from different field of study. Articles were selected and reviewed based on relevance and quality, highlighting studies demonstrating AI's impact on diagnosis and management.Results: AI‐powered diagnostic systems have ominously advanced, offering noninvasive and accessible diagnostic tool(s) that use extensive datasets to improve accuracy and efficacy across different populations. The development of dermatological databases is fundamental for training of different AI models. Despite these advances, challenges related to data privacy, regulatory oversight, and inclusivity in AI models persist. Addressing these challenges is essential for augmenting and optimizing AI's potential in dermatology.Conclusion: AI is set to transmute dermatology by augmenting diagnostic precision, customizing treatment plans, and making dermatological care more reachable, particularly in underprivileged areas. This review highlights the various advances made in AI for the diagnosis and management of inflammatory skin disorders, acknowledging the ethical and technical hurdles that need to be addressed.

Background: Infective and infectious dermatological diseases, which range from minor diseases like impetigo to serious diseases like deep fungal infections, pose significant public health issues. Given the growth of drug‐resistant microorganisms, it is critical to investigate novel techniques in dermatology. Artificial Intelligence (AI) has shown promising results in improving the diagnosis, treatment, and management of infectious skin disorders. This has the potential to significantly improve dermatological treatment by combining physician experience with data‐driven insights.Objective: This review will look into the existing uses and future possibilities of AI technologies in infectious dermatology, including machine learning and deep learning. Its goal is to highlight major advances, identify gaps in understanding and technical advancement, and recommend viable future research directions.Methods: A comprehensive literature search of the scientific literature was performed using well‐known databases such as PubMed, Google Scholar, and Embase. A specific set of phrases relevant to AI and infectious dermatology was used to ensure a thorough search. Articles were picked based on their relevance, timeliness, and quality, with a particular emphasis on research demonstrating how AI is being utilized to prevent, detect, diagnose, or manage infectious skin disorders.Results: AI has made significant contributions to the management of infection in dermatology. It has improved diagnostic accuracy, predictive modeling of drug resistance, and individualized care regimens. Deep learning is used to evaluate clinical images, predictive models are developed to forecast antibiotic resistance, and AI‐powered diagnostic tools for uncommon infections. The assessment also throws light on AI's role in pandemic preparedness and response.

Background: Discoid lupus erythematosus (DLE) is an autoimmune disease with multifactor etiology which develops in genetically susceptible patients. Rarely, DLE lesions can mimic other connective tissue disorders such as morphea. The growing application of soft tissue fillers is associated with increasing complications. Some substances used for soft tissue augmentation such as silicon implants may trigger lupus erythematosus diseases. Case report: Here we report a case of morphea‐like discoid lupus erythematosus developed several years after polyacrylamide dermal filler (PAAG) injection for facial rejuvenation. Conclusion: As noninvasive procedures like dermal filler injections are increasing worldwide, physicians may consider the long‐term probable side effects of these compounds.

A 34-year-old woman was referred with a history of psoriasiform plaques for 12 years skin biopsy was performed and a diagnosis of MF was confirmed and treatment with PUVA, prednisolon, methotrexate, topical ointment including ucerin, urea ad clobetasol were initiated. Significant improvement in all lesions were observed