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In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This article presents a computer imaging algorithm that may automate and enhance the Severity of Alopecia Tool scoring for alopecia areata through texture analysis of pediatric images.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

This study found a significant positive correlation between hair thickness and growth rate, with a notably slower growth rate observed in men with male pattern hair loss compared to healthy controls.

This study presents the first report on cloning and characterizing the full-length cDNA of SRD5A1 in Indian catfish (Clarias magur), revealing expression differences across reproductive phases and increased expression post-Ovatide administration in ovaries and testis.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.

In this study, a system was developed utilizing advanced image processing to analyze hair and scalp conditions, aiding professionals in diagnosing diseases like alopecia and monitoring treatment by extracting and comparing key parameters from microscopic images.

This mini-review summarizes the Trichoscan as a sensitive tool for measuring hair growth parameters, demonstrating its effectiveness in detecting treatment response in androgenetic alopecia but noting some practical limitations.

This study developed a high-resolution phototrichogram system that can automatically and accurately assess hair growth metrics in cosmetic trials, achieving over 90% correlation with manual measurements.

This study found that a hair tonic increased hair growth and reduced resting hair ratios in men with alopecia.

In this study, researchers identified two person-centered sexual function profiles among women, linked to physical and psychological factors, with PCOS showing greater, though not significant, presence in the dysfunction profile. The dysfunction was associated with higher adiposity and body-image distress.

This study compiled an archive of 684 genes associated with monogenic hair disorders, identifying previously unrecognized components of Hippo signaling and proposing a new biologically-grounded disease taxonomy.

This study developed a method to quantify hair loss in mice, aiding the evaluation of new treatments for alopecia.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This study identified significant changes in the expression of nine proteins in the nucleus accumbens of rats treated with finasteride, suggesting potential novel targets for its neuropsychiatric effects.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

In this study, phytochemical analysis and molecular docking suggested that bioactive compounds in several Ayurvedic herbs may interact persistently with hair growth and antifungal protein targets, indicating potential as plant-based treatments for dandruff and hair loss.

This study reveals that somatosensory neurons in mouse skin exhibit structural plasticity during hair-follicle regeneration, which may temporarily impair the reliability of encoding gentle touch.

This study introduces Cell Journey, a new platform for visualizing RNA velocity in 3D, which aims to better capture complex cellular transitions in single-cell datasets compared to current 2D methods.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

This study investigated the roles of long non-coding RNAs in mouse hair follicle stem cells, using sequencing to identify potential biomarkers and targets for treatments in both mice and humans.

This study found that low image resolutions allow expert clinicians to detect alopecia, but higher resolutions are necessary for identifying scarring and vellus hair, which may inform future image processing algorithms in dermatology.

This study found that a lightweight Convolutional Neural Network model can accurately and quickly determine natural hair tone from high-resolution images of hair roots, outperforming other popular methods.

This study identified 19 specific genes involved in root hair growth and morphogenesis in Arabidopsis, using a combination of computational and experimental methods.

In this paper, Chemistry42—a software integrating AI with computational and medicinal chemistry—demonstrated efficiency in designing novel molecular structures targeting DDR1 and CDK20, with properties validated in both in vitro and in vivo studies.