Pilot study of an automated method to determine Melasma Area and Severity Index
Summary Background Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter‐observer variab...
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| Veröffentlicht in: | British journal of dermatology (1951) 2015-06, Vol.172 (6), p.1535-1540 |
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| container_title | British journal of dermatology (1951) |
| container_volume | 172 |
| creator | Tay, E.Y. Gan, E.Y. Tan, V.W.D. Lin, Z. Liang, Y. Lin, F. Wee, S. Thng, T.G. |
| description | Summary
Background
Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter‐observer variability.
Objectives
To develop and validate a novel image analysis software designed to automatically calculate the area and degree of hyperpigmentation in melasma from computer image analysis of whole‐face digital photographs, thereby deriving an automated mMASI score (aMASI).
Methods
The algorithm was developed in collaboration between dermatologists and image analysis experts. Firstly, using an adaptive threshold method, the algorithm identifies, segments and calculates the areas involved. It then calculates the darkness. Finally, the derived area and darkness are then used to calculate mMASI. The scores derived from the algorithm are validated prospectively. Twenty‐nine patients with melasma using depigmenting agents were recruited for validation. Three dermatologists scored mMASI at baseline and post‐treatment using standardized photographs. These scores were compared with aMASI scores derived from computer analysis.
Results
aMASI scores correlated well with clinical mMASI pre‐treatment (r = 0·735, P |
| doi_str_mv | 10.1111/bjd.13699 |
| format | Article |
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Background
Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter‐observer variability.
Objectives
To develop and validate a novel image analysis software designed to automatically calculate the area and degree of hyperpigmentation in melasma from computer image analysis of whole‐face digital photographs, thereby deriving an automated mMASI score (aMASI).
Methods
The algorithm was developed in collaboration between dermatologists and image analysis experts. Firstly, using an adaptive threshold method, the algorithm identifies, segments and calculates the areas involved. It then calculates the darkness. Finally, the derived area and darkness are then used to calculate mMASI. The scores derived from the algorithm are validated prospectively. Twenty‐nine patients with melasma using depigmenting agents were recruited for validation. Three dermatologists scored mMASI at baseline and post‐treatment using standardized photographs. These scores were compared with aMASI scores derived from computer analysis.
Results
aMASI scores correlated well with clinical mMASI pre‐treatment (r = 0·735, P < 0·001) and post‐treatment (r = 0·608, P < 0·001). aMASI was reliable in detecting changes with treatment. These changes in aMASI scores correlated well with changes in clinician‐assessed mMASI (r = 0·622, P < 0·001).
Conclusions
This study proposes a novel approach in melasma scoring using digital image analysis. It holds promise as a tool that would enable clinicians worldwide to standardize melasma severity scoring and outcome measures in an easy and reproducible manner, enabling different treatment options to be compared accurately.
What's already known about this topic?
The Melasma Area and Severity Index (MASI) and modified MASI (mMASI) are the only two validated scores for assessing melasma severity.
They are essential to the evaluation of treatment response in trials and in the clinical setting.
However, MASI and mMASI scoring is subject to much inter‐observer variability and training is necessary to ensure consistent scoring.
What does this study add?
A novel image analysis software has been developed to derive automated a MASI scores.
Automated mMASI scores correlate well with clinician‐scored mMASI scores.</description><identifier>ISSN: 0007-0963</identifier><identifier>EISSN: 1365-2133</identifier><identifier>DOI: 10.1111/bjd.13699</identifier><identifier>PMID: 25641313</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Automation ; Computer applications ; Dermatology ; Facial Dermatoses - pathology ; Female ; Humans ; Hyperpigmentation ; Image processing ; Image Processing, Computer-Assisted - methods ; Male ; Melanosis - pathology ; Middle Aged ; Pilot Projects ; Software</subject><ispartof>British journal of dermatology (1951), 2015-06, Vol.172 (6), p.1535-1540</ispartof><rights>2015 British Association of Dermatologists</rights><rights>2015 British Association of Dermatologists.</rights><rights>Copyright © 2015 British Association of Dermatologists</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4619-3715268378c0e190b82fcba82da9dc727ee5177c24f95c022306c39e99f047a73</citedby><cites>FETCH-LOGICAL-c4619-3715268378c0e190b82fcba82da9dc727ee5177c24f95c022306c39e99f047a73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fbjd.13699$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fbjd.13699$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25641313$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tay, E.Y.</creatorcontrib><creatorcontrib>Gan, E.Y.</creatorcontrib><creatorcontrib>Tan, V.W.D.</creatorcontrib><creatorcontrib>Lin, Z.</creatorcontrib><creatorcontrib>Liang, Y.</creatorcontrib><creatorcontrib>Lin, F.</creatorcontrib><creatorcontrib>Wee, S.</creatorcontrib><creatorcontrib>Thng, T.G.</creatorcontrib><title>Pilot study of an automated method to determine Melasma Area and Severity Index</title><title>British journal of dermatology (1951)</title><addtitle>Br J Dermatol</addtitle><description>Summary
Background
Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter‐observer variability.
Objectives
To develop and validate a novel image analysis software designed to automatically calculate the area and degree of hyperpigmentation in melasma from computer image analysis of whole‐face digital photographs, thereby deriving an automated mMASI score (aMASI).
Methods
The algorithm was developed in collaboration between dermatologists and image analysis experts. Firstly, using an adaptive threshold method, the algorithm identifies, segments and calculates the areas involved. It then calculates the darkness. Finally, the derived area and darkness are then used to calculate mMASI. The scores derived from the algorithm are validated prospectively. Twenty‐nine patients with melasma using depigmenting agents were recruited for validation. Three dermatologists scored mMASI at baseline and post‐treatment using standardized photographs. These scores were compared with aMASI scores derived from computer analysis.
Results
aMASI scores correlated well with clinical mMASI pre‐treatment (r = 0·735, P < 0·001) and post‐treatment (r = 0·608, P < 0·001). aMASI was reliable in detecting changes with treatment. These changes in aMASI scores correlated well with changes in clinician‐assessed mMASI (r = 0·622, P < 0·001).
Conclusions
This study proposes a novel approach in melasma scoring using digital image analysis. It holds promise as a tool that would enable clinicians worldwide to standardize melasma severity scoring and outcome measures in an easy and reproducible manner, enabling different treatment options to be compared accurately.
What's already known about this topic?
The Melasma Area and Severity Index (MASI) and modified MASI (mMASI) are the only two validated scores for assessing melasma severity.
They are essential to the evaluation of treatment response in trials and in the clinical setting.
However, MASI and mMASI scoring is subject to much inter‐observer variability and training is necessary to ensure consistent scoring.
What does this study add?
A novel image analysis software has been developed to derive automated a MASI scores.
Automated mMASI scores correlate well with clinician‐scored mMASI scores.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Computer applications</subject><subject>Dermatology</subject><subject>Facial Dermatoses - pathology</subject><subject>Female</subject><subject>Humans</subject><subject>Hyperpigmentation</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Male</subject><subject>Melanosis - pathology</subject><subject>Middle Aged</subject><subject>Pilot Projects</subject><subject>Software</subject><issn>0007-0963</issn><issn>1365-2133</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10EFvFCEUB3BiNHZbPfgFDIkXe5j2ATMwHOtW15pqTao18UJYeBNnnRkqMNr99qLb9mAil3f5vT_wJ-QZgyNWzvF644-YkFo_IIsym4ozIR6SBQCoCrQUe2Q_pQ0AE9DAY7LHG1kzwcSCXHzsh5BpyrPf0tBRO1E75zDajJ6OmL8FT3OgHjPGsZ-QvsfBptHSk4i2aE8v8SfGPm_p2eTx5gl51Nkh4dPbeUA-v3n9afm2Or9YnS1PzitXS6YroVjDZStU6wCZhnXLO7e2LfdWe6e4QmyYUo7XnW4ccC5AOqFR6w5qZZU4IC93udcx_JgxZTP2yeEw2AnDnAyTrQRV_l8X-uIfuglznMrrDC9Xc8EbgKIOd8rFkFLEzlzHfrRxaxiYPy2b0rL523Kxz28T5_WI_l7e1VrA8Q786gfc_j_JvHp3ehdZ7Tb6lPHmfsPG70YqoRrz5cPKfL1cXrWnVysjxW9FOZLS</recordid><startdate>201506</startdate><enddate>201506</enddate><creator>Tay, E.Y.</creator><creator>Gan, E.Y.</creator><creator>Tan, V.W.D.</creator><creator>Lin, Z.</creator><creator>Liang, Y.</creator><creator>Lin, F.</creator><creator>Wee, S.</creator><creator>Thng, T.G.</creator><general>Blackwell Publishing Ltd</general><general>Oxford University Press</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>H94</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope></search><sort><creationdate>201506</creationdate><title>Pilot study of an automated method to determine Melasma Area and Severity Index</title><author>Tay, E.Y. ; Gan, E.Y. ; Tan, V.W.D. ; Lin, Z. ; Liang, Y. ; Lin, F. ; Wee, S. ; Thng, T.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4619-3715268378c0e190b82fcba82da9dc727ee5177c24f95c022306c39e99f047a73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Computer applications</topic><topic>Dermatology</topic><topic>Facial Dermatoses - pathology</topic><topic>Female</topic><topic>Humans</topic><topic>Hyperpigmentation</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Male</topic><topic>Melanosis - pathology</topic><topic>Middle Aged</topic><topic>Pilot Projects</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tay, E.Y.</creatorcontrib><creatorcontrib>Gan, E.Y.</creatorcontrib><creatorcontrib>Tan, V.W.D.</creatorcontrib><creatorcontrib>Lin, Z.</creatorcontrib><creatorcontrib>Liang, Y.</creatorcontrib><creatorcontrib>Lin, F.</creatorcontrib><creatorcontrib>Wee, S.</creatorcontrib><creatorcontrib>Thng, T.G.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>British journal of dermatology (1951)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tay, E.Y.</au><au>Gan, E.Y.</au><au>Tan, V.W.D.</au><au>Lin, Z.</au><au>Liang, Y.</au><au>Lin, F.</au><au>Wee, S.</au><au>Thng, T.G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pilot study of an automated method to determine Melasma Area and Severity Index</atitle><jtitle>British journal of dermatology (1951)</jtitle><addtitle>Br J Dermatol</addtitle><date>2015-06</date><risdate>2015</risdate><volume>172</volume><issue>6</issue><spage>1535</spage><epage>1540</epage><pages>1535-1540</pages><issn>0007-0963</issn><eissn>1365-2133</eissn><abstract>Summary
Background
Objective outcome measures for melasma severity are essential for the evaluation of severity as well as results of treatment. The modified Melasma Area and Severity Index (mMASI) score is a validated tool for assessing melasma severity but is often subject to inter‐observer variability.
Objectives
To develop and validate a novel image analysis software designed to automatically calculate the area and degree of hyperpigmentation in melasma from computer image analysis of whole‐face digital photographs, thereby deriving an automated mMASI score (aMASI).
Methods
The algorithm was developed in collaboration between dermatologists and image analysis experts. Firstly, using an adaptive threshold method, the algorithm identifies, segments and calculates the areas involved. It then calculates the darkness. Finally, the derived area and darkness are then used to calculate mMASI. The scores derived from the algorithm are validated prospectively. Twenty‐nine patients with melasma using depigmenting agents were recruited for validation. Three dermatologists scored mMASI at baseline and post‐treatment using standardized photographs. These scores were compared with aMASI scores derived from computer analysis.
Results
aMASI scores correlated well with clinical mMASI pre‐treatment (r = 0·735, P < 0·001) and post‐treatment (r = 0·608, P < 0·001). aMASI was reliable in detecting changes with treatment. These changes in aMASI scores correlated well with changes in clinician‐assessed mMASI (r = 0·622, P < 0·001).
Conclusions
This study proposes a novel approach in melasma scoring using digital image analysis. It holds promise as a tool that would enable clinicians worldwide to standardize melasma severity scoring and outcome measures in an easy and reproducible manner, enabling different treatment options to be compared accurately.
What's already known about this topic?
The Melasma Area and Severity Index (MASI) and modified MASI (mMASI) are the only two validated scores for assessing melasma severity.
They are essential to the evaluation of treatment response in trials and in the clinical setting.
However, MASI and mMASI scoring is subject to much inter‐observer variability and training is necessary to ensure consistent scoring.
What does this study add?
A novel image analysis software has been developed to derive automated a MASI scores.
Automated mMASI scores correlate well with clinician‐scored mMASI scores.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>25641313</pmid><doi>10.1111/bjd.13699</doi><tpages>6</tpages></addata></record> |
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| identifier | ISSN: 0007-0963 |
| ispartof | British journal of dermatology (1951), 2015-06, Vol.172 (6), p.1535-1540 |
| issn | 0007-0963 1365-2133 |
| language | eng |
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| source | MEDLINE; Oxford Academic Journals (OUP); Wiley Online Library All Journals |
| subjects | Algorithms Automation Computer applications Dermatology Facial Dermatoses - pathology Female Humans Hyperpigmentation Image processing Image Processing, Computer-Assisted - methods Male Melanosis - pathology Middle Aged Pilot Projects Software |
| title | Pilot study of an automated method to determine Melasma Area and Severity Index |
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