Detection of Vitiligo Through Machine Learning and Computer‐Aided Techniques: A Systematic Review.
Background and Objective: Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%–1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy loss of skin pigmentation, o...
| Publicado en: | BioMed Research International Vol. 2024; pp. 1 - 16 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/19/2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181777915&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181777915 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/19/2024 vid: 2024 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 181777915 181777915 181777915 10.1155/bmri/3277546 181777915 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Detection of Vitiligo Through Machine Learning and Computer‐Aided Techniques: A Systematic Review. aug: au: Tanvir, Sania Syed, Sidra Abid Hussain, Samreen Zia, Razia Rashid, Munaf Zahid, Hira Shah, Sajid affil: Faculty of Electrical and Computer Engineering,, Biomedical Engineering Department,, Sir Syed University of Engineering and Technology,, Karachi, Pakistan, ssuet.edu.pk sug: subj: Vitiligo Diagnosis Diagnosis, Computer Assisted Methods Machine Learning Human Systematic Review PubMed Validity Vitiligo Therapy Treatment Outcomes Forecasting Algorithms ab: Background and Objective: Vitiligo is a chronic skin damage disease, triggered by differential melanocyte death. Vitiligo (0.5%–1% of the population) is one of the most severe skin conditions. In general, the foundation of the condition of vitiligo remains gradual patchy loss of skin pigmentation, overlying blood, and sometimes mucus. This paper provides a systematic review of the relevant publications and conference papers based on the subject of vitiligo diagnosis and confirmation through computer‐aided machine learning (ML) techniques. Materials and Methods: A search was conducted using a predetermined set of keywords across three databases, namely, Science Direct, PubMed, and IEEE Xplore. The selection process involved the application of eligibility criteria, which led to the inclusion of research published in reputable journals and conference proceedings up until June 2024. These selected papers were then subjected to full‐text screening for additional analysis. Research publications that involved application of ML techniques with targeted population of vitiligo were selected for further systematic review. Results: Ten selected and screened studies are included in this systematic review after applying eligibility criteria along with inclusion and exclusion criteria applied on initial search result which was 244 studies based on vitiligo. Priority is given to those studies only which use ML techniques to perform detection and diagnosis on vitiligo‐targeted population. Data analysis was carried out only from the selected and screened research articles that were published in authentic journals and conference proceedings. Conclusion: The importance of applying ML techniques in the clinical diagnosis of vitiligo can give more accurate results and at the same also eliminate the need of biased human judgement. Based on a comprehensive examination of the research, encompassing the methodologies employed and the metrics utilized to assess outcomes, it was determined that there is a need for further research and investigation regarding the application of ML algorithm for the detection and diagnosis of vitiligo with different datasets and more feature extraction. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|