Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration

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This review is intended as a roadmap for clinicians and researchers interested in trichoscopy-centered computational imaging for hair and scalp disorders. It compares available digital systems and algorithmic strategies, summarizing performance metrics and discussing practical implementation in clinical workflows.

Abstract

This scoping review summarizes current computational image analysis and artificial intelligence (AI) approaches for the assessment of hair and scalp disorders, with emphasis on quantitative trichoscopy and operator-independent evaluation. A deep Medline search was performed using a citation network-based approach using MeSH terms and complementary keywords covering diagnostic imaging, trichoscopy/videodermoscopy, image processing, algorithms, AI, and mobile/smartphone-based workflows. Overall, relatively few studies assess algorithms in real-world clinical pathways, and much of the retrieved literature is predominantly pre-clinical or methodology-driven. In parallel, commercially available AI-assisted trichoscopy platforms have little or no traceable peer-reviewed evidence; their validation methods and underlying datasets are often proprietary, undisclosed, and not directly comparable, limiting independent verification and cross-platform benchmarking. The most mature academic applications focus on follicular unit quantification (hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping), mainly using convolutional neural networks with object detection and instance segmentation. In conclusion, AI-assisted trichoscopy may support a shift toward standardized quantitative outputs, but clinical translation remains early and constrained by small or proprietary datasets, heterogeneous acquisition/annotation protocols, limited external validation, and scarce prospective studies.

1. Introduction

The hair follicle (HF) is a highly specialized, self-renewing mini-organ that cycles through defined stages of anagen, catagen, telogen, latency, and miniaturization, making it an ideal model system for studying tissue dynamics and systems biology [1]. Each follicle operates as an independent biological entity capable of undergoing complete regenerative cycles, yet in human scalp skin, follicles are not randomly distributed, but rather organized into follicular units (FUs), i.e., compact anatomical and functional clusters comprising one to four terminal hair follicles that share a common pilosebaceous apparatus, vascular network, and arrector pili muscle insertion.

Each FU typically comprises one to four terminal hairs (depending on genetic predisposition and scalp location), associated sebaceous glands, an arrector pili muscle, and a shared infundibular ostium. This architectural and functional complexity enables remarkable regenerative capacity but also renders the HF susceptible to multiple regulatory failures that underlie the most common hair disorders [2,3].

Dysregulation of the hair cycle can result in a wide range of clinical presentations, among the most common androgenetic alopecia (AGA), characterized by progressive follicular miniaturization and a shortened anagen phase [4,5] and alopecia areata (AA), an autoimmune condition causing non-scarring hair loss [6,7,8]. Accurate assessment of these changes is critical, as subtle variations in hair shaft diameter, follicular density, and anagen–telogen ratio often precede visible clinical signs [9].

Therefore, not only qualitative but quantitative evaluation of the FU and its dynamics is essential for diagnostic classification but also for monitoring disease progression and therapeutic response over time [10,11,12].

In clinical and research settings, follow-up of the main follicular and hair parameters, such as hair shaft diameter, hair count per cm², proportion of vellus versus terminal hairs, and hair growth rate, is the cornerstone for monitoring and evaluating treatment efficacy in alopecic conditions [13,14]. To address this, a variety of systems have been developed over the years to perform quantitative analysis, ranging from early analogue and digital phototrichograms to modern high-resolution trichoscopic devices [15,16]. Traditional methods include manual hair counting on magnified scalp images, ink-marking of defined scalp areas (unit-area trichogram), and cross-sectional trichometry for measuring mean hair thickness [17,18]; it is imaginable that these methods are intra- and inter-operator variable, time-consuming, and often less reliable. Later, semi-automated solutions combined digital imaging with algorithmic hair segmentation, but initially these advances still relied on supervised correction and validation by expert operators, often involving labor-intensive frame-by-frame hair counting or manual delineation of analysis areas [19]. This operator dependence not only introduced subjectivity but also limited reproducibility, particularly in teledermatology and multicenter trials, where standardization is crucial [20].

Finally, artificial intelligence (AI) and deep learning (DL) have been heavily implemented as transformative tools to overcome these constraints by automating follicle detection, segmentation, and classification to derive density and growth metrics. This has already been implemented in semi-supervised and unsupervised digital tools such as standalone tools or plugins in digital dermato/trichoscopic devices [19]. Such systems drastically reduce observer variability while providing highly reproducible quantitative data, and are based mainly on convolutional neural networks (CNNs), object detection frameworks such as YOLOv4, EfficientNet, and DetectoRS, as well as instance segmentation architectures like Mask R-CNN, and have been trained on large trichoscopic datasets to compute follicular density, shaft diameter distribution, and local severity mapping. These approaches potentially enable more automated and operator-independent analysis and longitudinal follow-up. The reachable potential is to have highly reproducible quantitative data and to reduce observer variability compared with manual evaluation.

Collectively, these innovations mark a paradigm shift from descriptive trichoscopy to quantitative, standardized, and reproducible digital trichology.

Building upon this evolution, the present scoping review aims to provide clinicians and researchers with a structured overview of the current AI-based analytical systems applied to hair and scalp disorders, focusing on the current research trends/topics and highlighting the current missing directions that could be addressed by future research.

2. Materials and Methods

A scoping literature search was conducted on the PubMed database to identify publications on hair diseases assessed using diagnostic imaging and advanced digital methods. The Boolean operators AND/OR were used to combine the terms and maximize sensitivity. To automatically evaluate the current research trends, we proposed a novel search approach, building a citation network from the most common and informative MeSH terms in literature. We downloaded the full PubMed database from the official repository, containing scientific papers published up to 2 October 2025. We used the reported MeSH identifiers to extract the papers’ topics and match those of interest, with the MeSH identifier being the list of topics assigned to each paper by PubMed. We considered as relevant papers those assigned MeSH identifiers matching the following query: (hair diseases/diagnostic imaging OR hair/diagnostic imaging OR hair follicle OR dermatoscopes) AND (machine learning OR artificial intelligence OR software OR algorithm OR image-processing, computer-assisted, mobile-applications, smartphone). We restricted our analysis to the PubMed database only since it provides robust and well-structured APIs for our automated search.

The search was restricted to English-language studies, and all identified records were assessed for eligibility according to PRISMA guidelines. No exclusion criteria were implemented for conference papers or proceedings. Literature results were extracted using a network-based approach combined with custom machine-learning software for the identification of related topics/keywords. The set of articles and associated data was independently reviewed by two independent experts to ensure accuracy and reduce bias. Any disagreements were resolved by discussion or consultation with a third reviewer.

Citation Network Analysis

Using the papers extracted with this procedure, we built a citation network, connecting the selected papers according to their citations, i.e., we considered a network link from a paper to another when the first cited the latter [21,22]. Each node of the obtained network, i.e., a paper, was analyzed using a Natural Language Processing (NLP) pipeline: selecting the title and abstract sentences, we pre-processed the text, discarding the irrelevant words (rejection criteria, e.g., conjunctions, adjectives, adverbs, etc.), identifying a subset of 5 keywords which identified as best as possible the topic discussed in the work [23,24].

Starting from this network structure, a graph-based analysis was performed, evaluating the presence of possible disjoined connected components and centrality measurements: the “literature importance” of a paper could be quantified by the number of papers which cited it (i.e., a degree centrality of the node graph), or by its importance in connecting two highly dense components (i.e., the betweenness centrality of the node graph). This approach helps us in identifying the most relevant and central research, with the possibility of discarding novel articles or works which propose simply improving available techniques. We would like to stress that in our review, we are mainly interested in covering the principal and/or missing topics about these arguments, and a paper which produces a unique citation does not represent a significant improvement in our discussion.

Furthermore, a community detection analysis was performed according to the Louvain method, aiming to identify collections of topics and possible gaps in literature organization, i.e., the absence of direct connections between communities of different arguments which highlight missing documentations of longitudinal topics. Default resolution parameters were used, and the resulting communities were interpreted as thematic clusters representing groups of papers sharing similar citation patterns [25]. Potential sources of bias were considered during study appraisal; however, a formal tool-based risk-of-bias table is not reported due to limited applicability across the heterogeneous study designs included. The entire list of papers identified by our method, alongside the data about their positioning in the network structure, was manually analyzed by two independent reviewers to guarantee the correctness of the extracted information.

3. Results

The search strategy yielded 1261 records through the Medline database. Due to the single database search, no duplicates could be included. We conducted a preliminary screening of titles and abstracts, and potentially relevant articles were assessed for eligibility according to predefined inclusion and exclusion criteria automatically checked by our search system. We remark that the principal exclusion criteria implemented involved the removal of articles linked to the others (core) with a single and unique citation.

Following full-text evaluation, 58 studies met the inclusion criteria and were retained for qualitative synthesis. The main reasons for exclusion included:

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Studies focusing exclusively on basic biological or molecular mechanisms without imaging or AI method implementation;

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Animal or in vitro investigations not translatable into clinical or imaging settings;

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Reports addressing hair growth or follicle physiology without the use of diagnostic imaging or computational analysis;

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Studies lacking quantitative or visual diagnostic data relevant to trichoscopic or dermoscopic evaluation.

The PRISMA flow diagram [Figure 1] summarizes the selection process:

• Records identified through database searching: 1261;
• Records after screening and eligibility assessment: 58 included;
• Records excluded: 1203.

This process ensured the inclusion of the most relevant publications addressing imaging and computational and AI applications in the evaluation of hair and scalp disorders.

• Results—Citation network

The pruned citation network consisted of 58 nodes (articles) and 91 edges, where each edge represented a shared citation between pairs of studies. This pruning process reduced a larger initial selection/network to its most informative structure (core), retaining articles with meaningful bibliographic similarity while improving interpretability and reducing noise [Figure 1] [26].

Overall, the network showed moderate connectivity, with a mean node degree of 3.1, indicating that each article shared approximately three common references with other studies on average. The degree distribution was right-skewed, with a small number of highly connected nodes (maximum degree = 17) acting as hubs, and a larger number of peripheral articles with few shared citations. This pattern suggests the presence of influential or methodologically central publications that bridge multiple thematic areas.

Betweenness centrality values were generally low (mean ≈ 0.04), indicating that most articles contributed locally to knowledge clusters rather than serving as dominant global bridges. However, a limited subset of nodes exhibited higher betweenness, highlighting their role in connecting otherwise distinct research themes.

Community detection identified seven distinct communities, reflecting coherent thematic groupings of articles based on shared citation patterns. Community sizes ranged from 4 to 12 articles, indicating both well-established and more specialized research strands within the field.

The communities were meaningfully associated with the MeSH descriptor profiles of the articles:

• Larger communities were characterized by methodological and technological descriptors, such as Algorithms, Neural Networks, Computer-Assisted Image Analysis, and Radiography, reflecting clusters of studies focused on automated or imaging-based hair assessment.
• Other communities showed stronger associations with clinical and biological descriptors, including Hair Follicle, Scalp, Alopecia, Humans, and sex-specific terms, suggesting clinically oriented research groups.
• Smaller communities appeared more specialized, often combining niche methodological approaches with specific clinical applications.
• The separation between communities, combined with limited cross-community connectivity, indicates that while the field shares common methodological foundations, research remains partially compartmentalized into subdomains with distinct citation traditions.

At the node level, highly connected articles tended to combine broad methodological descriptors with clinical relevance, positioning them as reference points across multiple communities. In contrast, low-degree nodes often corresponded to more recent or narrowly focused studies, contributing depth within specific subtopics rather than breadth across the network [Figure 2].

• Data synthesis

The final dataset of 58 studies [Appendix A], published between the earliest identified record and September 2025, reflects the progressive integration of digital technologies in trichology.

The included literature encompasses both quantitative and qualitative investigations focusing on diagnostic imaging, computer-assisted analysis, and AI-based tools for hair disease assessment.

To show the actual state of the art, results synthesis was organized into three main conceptual domains:

• Studies on the pre-clinical features of scalp diseases:
  Biological and histological models assessing and targeting scalp diseases causes and pursuing diagnostical models.
• Computer-assisted and algorithmic methods:
  Research employing image processing and software-driven analysis for segmentation, measurement, and morphological characterization of follicular structures.
  Studies developing or validating AI algorithms for pattern recognition, disease classification, and automated image interpretation in trichoscopy.
• Software implementing AI techniques:
  Works exploring portable trichoscopy, smartphone-based imaging, and remote diagnostic workflows integrating artificial intelligence or cloud-based analytics.

Studies on the pre-clinical feature of scalp diseases using imaging and computational models.

• Omics aspects of the HF

The HF provides a unique model for exploring tissue-level dynamics and molecular regulation of hair cycling. Computational approaches, such as follicular automaton, simulate stochastic transitions of thousands of follicles across the anagen, catagen, and telogen stages, effectively reproducing the graded patterns observed in androgenetic alopecia [27].

At the molecular level, studies have elucidated the central role of Wnt/β-catenin signaling in controlling the telogen-to-anagen transition [28,29]. Telomerase reverse transcriptase (TERT) also promotes anagen independently of its catalytic activity, inducing proliferation within the interfollicular epidermis and engaging transcriptional programs overlapping with Myc and Wnt signaling [30].

Chronobiological regulation also plays a critical role in follicular function: hair follicles possess peripheral circadian clocks, and gene expression can be measured noninvasively from plucked hair samples, allowing accurate monitoring of circadian phase [31,32].

2.

Structural model of the HF

High-resolution imaging techniques provide unprecedented structural and molecular insight into hair follicles: three-dimensional reconstruction of serially sectioned tissue, complemented by Micro CT, has refined our understanding of the arrector pili (AP) muscle. The AP is now recognized as a single, branched muscular structure that aligns with follicular orientation and inserts into all follicles within a follicular unit, with quantifiable metrics for depth and length in intact skin [33,34].

3.

Main limitations

Organ-cultured human hair follicles have been pivotal for mechanistic studies [35]. Nevertheless, a major limitation remains: the reliance on murine models, which fail to replicate key human-specific pathologies. These challenges underscore the importance of human-centered models, such as organ-cultured follicles and bioengineered skin systems, despite technical limitations in monitoring follicular miniaturization and regeneration [36].

• Computer-assisted algorithmic methods

AI applications, particularly deep learning, have been pivotal in moving hair pathology diagnosis from qualitative assessment toward precise, quantitative measurement regarding the external aspects of the FU. Modern DL algorithms focus on reliably detecting and classifying hair follicles, overcoming limitations associated with overlapping shafts, image noise, and variable acquisition conditions.

Comparative studies evaluating object detection frameworks, such as YOLOv4, EfficientDet, and DetectoRS, have demonstrated the feasibility of automated HDM. YOLOv4 achieved the highest detection performance, with a mean Average Precision (mAP) of 58.67% and reduced rates of redundant follicle detection [37].

More complex DL models, including Mask R-CNN with ResNet-101 feature extraction, have been employed to segment follicles and classify them into clinically relevant categories—healthy, normal, or severe—based on parameters such as follicle size, hair count, and thickness. These models generate local hair loss severity indices (P) and aggregate severity maps (Pavg) that can be visualized as heatmaps, providing a spatial overview of hair loss progression across the scalp [37] [Figure 3].

Beyond follicle-level quantification, DL-based classifiers have demonstrated high accuracy in multi-disorder categorization. An enhanced Xception-based network achieved a diagnostic accuracy of 92% across six scalp and hair conditions, including alopecia, folliculitis, dissecting cellulitis, effluvium, psoriasis, and acne keloidalis nuchae [38]. Early methods combining Grid Line Selection and eigenvalue computation to extract hair loss features (HLFs) from smartphone-acquired microscopy images also demonstrated an average accuracy of 96.51% for hair count, illustrating the potential for AI-assisted self-monitoring of alopecia [39].

A key advantage of these systems is their objectivity and reproducibility, which reduce inter-clinician variability and improve longitudinal tracking of hair disorders. The adoption of explainable AI (XAI) techniques, such as Grad-CAM and saliency mapping, further enhances interpretability by visualizing which features influence network decisions, a critical requirement for clinical integration [38] [

Table 1. Overview of artificial intelligence–based studies for trichoscopic and scalp image analysis in hair and scalp disorders, summarizing study design, database characteristics, data source, reported outcomes, and availability of comparative evaluations.

Study	Year	Type	Numerosity of DB	DB Source	Reported Outcomes	Comparative Data Available	Values
Kim M. et al.—Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks (10.3390/s22020650) [37]	2022	Retrospective AI validation study	4492 images; 817 patients	National Information Society Agency (Korea) dataset; restricted to Korean male cohort	Mean Average Precision (mAP) for follicle detection	Yes—comparison among YOLOv4, EfficientDet, DetectoRS	YOLOv4: 58.67% mAP (highest)
Seo S. and Park J.—Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue (10.1155/2020/6908018) [39]	2020	Retrospective AI validation study	100	n-house scalp microscopy images captured with Galaxy Tab S4 + microscope (20×–800×; two microscope types)	Hair feature extraction (HLF): hair count, follicle count, hair thickness; accuracy reported	No	Mean accuracy: 96.51%; Table 1 (mean): hair truth 19.59 vs. pred 19.04 (diff 3.13); follicle truth 8.15 vs. pred 8.49 (diff 1.44); mean thickness pred 61.98; max error: hair 16, follicle 4.
Kim JH. et al.—Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN (10.3390/jimaging8100283) [40]	2022	Retrospective AI development/validation study	600 RGB images; 10 male subjects	Proprietary acquisition (microscopic scalp imaging)	Follicle instance segmentation + follicle status classification + severity heatmap	No head-to-head model comparison reported as primary aim	Train/test split reported (450/150); performance metrics reported in paper
Gao M. et al.—Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia (10.2340/actadv.v101.564) [41]	2022	Retrospective AI framework + predictive model	2910 trichoscopic images; 582 subjects (528 AGA + 54 controls)	Proprietary clinical dataset; no shaving	Automated hair density + diameter distribution extraction; BASP prediction model	Partially (internal model comparisons/variants)	Dataset split reported (2310 train/300 val/300 test; no participant overlap)
Di Fraia M. et al.—A Machine Learning Algorithm Applied to Trichoscopy for AGA Severity Assessment (10.5826/dpc.1303a136) [42]	2023	Retrospective ML study	N images: NR; 200 patients (100 M, 100 F)	Proprietary outpatient cohort; Trichoscale Pro®-based parameters	Mild vs. moderate–severe AGA classification + severity probability index	No	Accuracy: 94.3% (train), 90.0% (test); AUC: 0.99 (train), 0.95 (test)
Suh MJ. et al.—Automated Early Detection of Androgenetic Alopecia Using Deep Learning on Trichoscopic Images from a Korean Cohort (10.12771/emj.2025.00486) [43]	2025	Retrospective DL model development/validation	318 trichoscopic scalp images; N patients: NR	Ewha Womans University Medical Center; dataset not shared (privacy)	Binary classification: BASP 0 vs. BASP 1–3 (early AGA)	No	Model: ResNet-18 (ImageNet pretrained), fine-tuned; class distribution 159 vs. 159

].

• Software implementing AI processes

Early proof-of-concept work and available consumer apps demonstrated the feasibility of portable, smartphone-assisted trichoscopy as a technological precursor to current commercial AI-driven platforms.

Most of these systems were primarily designed for local image acquisition and transmission to remote specialists for manual evaluation, without standardized algorithms or public datasets, and the reported outcomes focused on hair density and shaft-diameter estimation, as well as diagnostic concordance between on-site and remote assessments.

Ground-truth validation in these studies relied exclusively on manual expert annotation, with no histologic or cross-device calibration. Despite methodological limitations, these experiments established the conceptual foundation for tele-trichoscopy and digital data capture in trichology [

Table 2. Proof-of-concept studies and currently available AI-assisted trichoscopy platforms, summarizing system type, datasets, validation methods, and reported outcomes. Academic systems demonstrate the feasibility of automated hair morphometric analysis, while commercial platforms extend these approaches to real-time, smartphone-based scalp assessment, often relying on proprietary datasets and non-public validation.

Study (PMID)	System	DOI/Link	Year	Type	Developer/Manufacturer	Dataset/Sample Size	Ground Truth/Validation	Reported Outcomes	Data Available
A novel automated approach to rapid and precise in vivo measurement of hair morphometrics using a smartphone (34251055) [44]	NR	10.1111/srt.13076	2021	Portable Dermoscope + laptop + Matlab®		50 patients	Manual expert counting	Automatic: 24 s, manual: 9.2 min; equivalent within 20% (density/diameter) on 50 scalps (p < 0.001 correlation)	Yes
Development and qualification of a machine learning algorithm for automated hair counting (34426987) [19]	HairMetrix®	10.1111/ics.12735	2021	AI/ML phototrichogram analysis (deep CNN)	Procter & Gamble + Canfield Scientific (Fairfield, NJ, USA)	288 manually annotated phototrichogram images (hair location/length)	Semi-manual Canfield HairMetrix; high reproducibility agreement	Automated count/length in seconds (vs. months manual); 100× cost/time reduction	Yes
Consumer app (no PMID)	Hair AI Scanner	https://apps.apple.com/it/app/hair-ai-hair-scanner-care/id6749930117 (accessed on 20 March 2026)	2025	Smartphone AI scanner	DO Mai Ly/AIany	NR (proprietary)	NR (no clinical validation)	Density/porosity/thickness/split ends/scalp health; personalized plans	No
Consumer app (No PMID)	HairLens AI	https://hairlensai.com/ (accessed on 20 March 2026)	2025	Smartphone AI multi-angle hair/scalp scanner	Usman Yousaf	170,000+ clinical images from 12 international centers	No published clinical validation (claims 94% diagnostic accuracy, 95% expert correlation)	Regional density (170 hairs/cm2), growth phases (85% anagen), treatment matching (FUE 98%, PRP 85%), health scores	No
Medical Device (no PMID)	Trichoschale®/TrichoLAB®/skeen (FotoFinder Ecosystem;)	https://www.tricholab.com/ (accessed on 20 March 2026)	2023–2024	3D imaging + cloud analytics	FotoFinder Systems (Warsaw, Poland)	Proprietary (commercial)	Manual + software metrics; internal QA	Hair density, shaft diameter, empty follicle detection	No
Medical Device (No PMID)	CASLite Nova	https://www.catseyetech.in/caslite.html (accessed on 20 March 2026)	2016		Cats Eye Systems & Solutions Pvt. Ltd. (Mumbai, India)			Claims: density, thickness, alopecia type	No

].

For completeness, this section also includes a qualitative synthesis of commercially available AI-assisted trichoscopy platforms, which, although not systematically represented in PubMed-indexed literature, constitute an integral part of the current diagnostic landscape.

• Considerations and limits on the software and algorithms reviewed

The analysis of AI-driven trichoscopy software highlights a clear dichotomy between academic, algorithm-focused studies and commercially available platforms. Academic studies predominantly aim to validate specific computer-assisted or deep learning methods under controlled conditions, often focusing on a single diagnostic task such as hair density measurement, follicle detection, or severity classification. These works typically rely on relatively small, single-center datasets, with ground truth defined by expert manual annotation and internal validation procedures.

In contrast, commercial platforms integrate AI algorithms into end-to-end diagnostic ecosystems that combine image acquisition hardware, cloud-based analytics, and automated reporting. While these systems offer clear advantages in terms of usability, real-time feedback, and scalability, their validation strategies frequently rely on proprietary datasets and internal quality assurance processes that are not independently verifiable. As a result, direct methodological comparison with academic models is often not feasible.

Another relevant consideration is that most AI-based software systems, both academic and commercial, focus on external follicular unit features, such as hair density, shaft diameter, vellus-to-terminal ratio, and growth phase estimation. Although these parameters are clinically relevant, they represent indirect surrogates of hair pathology and may not fully capture the biological complexity of follicular disorders. Furthermore, differences in image acquisition protocols, magnification, lighting, and scalp preparation introduce variability that may limit cross-platform reproducibility.

Finally, while explainable AI techniques and user-friendly visual outputs (e.g., heatmaps, severity indices) improve interpretability and clinical acceptance, their implementation remains heterogeneous. Only a minority of systems explicitly report explainability strategies or formally assess their impact on diagnostic confidence and decision-making.

4. Discussion

This review shows that image analysis and artificial intelligence are moving toward progressively reshaping scalp and hair disorder disease assessment, mainly by shifting from qualitative interpretations to more reproducible quantitative outputs. However, the current evidence is still driven by a limited number of datasets, which are often proprietary, restricted-access, or not publicly disseminated. As a result, the few academic non-profit studies and the several software tools and commercial applications developed are based mostly on small and non-transparent databases, making independent verification difficult and slowing meaningful cross-study benchmarking.

Across the retrieved literature, the most mature and reproducible use cases remain follicular unit quantification—hair density, shaft diameter distribution, vellus-to-terminal ratio, and severity mapping—typically implemented through object detection and instance segmentation pipelines. Importantly, these computational approaches are not unique to trichology: the same methodological backbone has already been widely adopted in dermatologic imaging, including melanoma assessment, aesthetic skin analysis, and chronic wound quantification. In this sense, trichoscopy follows an established technological trajectory rather than represents a completely novel AI domain.

Artificial intelligence–driven image analysis has advanced to a more mature stage in several dermatological domains [45], particularly melanoma detection, psoriasis severity assessment, and wound care monitoring [46,47,48,49,50]. In these areas, the availability of large annotated datasets, standardized imaging protocols, and shared benchmarking initiatives has enabled the development and validation of robust deep-learning models. This progress is also partly related to the broader clinical relevance of these conditions. For example, melanoma screening is a cornerstone of dermatological practice, while psoriasis, due to its high prevalence in the general population, attracts strong clinical and research interest across multiple disciplines [51,52]. Moreover, from a diagnostic perspective, these conditions often involve relatively well-defined classification tasks or measurable outcomes, such as distinguishing melanoma from benign lesions or quantifying psoriasis severity scores [53]. For instance, melanoma classification systems trained on dermoscopic repositories have demonstrated high diagnostic performance in controlled settings, while AI approaches in wound care are increasingly applied for automated segmentation, tissue classification, and longitudinal monitoring of chronic wounds [54,55,56].

Nevertheless, the methodological frameworks used in trichoscopic AI—such as convolutional neural networks for object detection, instance segmentation, and image classification—are closely aligned with those already established in other dermatological imaging fields [57]. Therefore, the experience gained in melanoma imaging, psoriasis assessment, and wound care analytics may provide a useful roadmap for future developments in AI-assisted trichoscopy, particularly through the creation of shared datasets, harmonized acquisition protocols, and transparent validation frameworks [58,59,60].

As said, the major limitation is that even when data are available, studies remain difficult to compare. Target definitions, acquisition protocols, annotation procedures, and evaluation metrics are highly heterogeneous, and only a minority of reports clearly describe both the number of images and the number of patients. Ground truth is almost always based on single-center expert annotation, rather than harmonized multi-center reference standards, increasing the risk of center-specific bias and reducing generalizability across devices and populations. For clinical translation, the field would benefit from shared benchmarking datasets (or at least interoperable evaluation frameworks), device-aware calibration, and standardized reporting of dataset composition and annotation quality.

We also acknowledge that the network-based pruning approach possibly discards good-quality papers that are too peripheral or too recent. While this approach is out of the standard for traditional systematic reviews, it may actually enhance the quality of a scoping review, allowing the selection of the core literature for the topic of interest, discarding too-niche or too-recent literature which may become influential in the future but does not reflect the broad scope of the current knowledge.

Beyond measurement tasks, a smaller body of academic work has explored AI systems aimed at multi-condition scalp disease classification and diagnostic decision support. While these studies suggest technical feasibility, they remain far from real-world adoption: labels are inconsistent, external validation is rare, and performance is seldom tested in prospective “human-in-the-loop” settings where clinicians interact with the tool under routine constraints. At present, diagnostic AI in trichology should therefore be interpreted as an emerging academic direction rather than a validated real-life solution, since external validation remains limited, prospective studies assessing workflow integration are scarce, and real-world clinical performance has yet to be robustly established.

5. Conclusions

Artificial intelligence is increasingly supporting scalp image assessment by converting trichoscopic and clinical photographs into quantitative, more reproducible outputs. Most current approaches are measurement-driven and rely on established computer vision architectures (such object detection and instance segmentation for follicular unit quantification [61,62]) like those already widely adopted in other dermatologic imaging fields (e.g., melanoma assessment, aesthetic skin analysis, wound measurement [54,63,64,65]).

AI-assisted trichoscopy represents the most clinically actionable domain, enabling automated extraction of follicular metrics such as hair density, shaft diameter distribution, and vellus-to-terminal ratio, and providing intuitive outputs (e.g., severity heatmaps) that may facilitate follow-up and longitudinal monitoring. However, translation remains constrained by the limited number of available datasets, which are often proprietary or restricted-access and have served as the basis for several software tools and commercial applications. Even when data are reported, heterogeneity in acquisition protocols, annotation rules, and evaluation metrics limit comparability and reduces reproducibility across studies.

In contrast, AI systems aimed at multi-condition scalp disease classification and diagnostic decision support remain largely academic proof-of-concept efforts, with limited external validation and scarce prospective testing in real-world workflows. Future progress will depend on standardized imaging procedures, transparent reporting of dataset composition and annotations, and shared benchmarking frameworks enabling robust cross-device and cross-center validation aligned with clinically meaningful endpoints.