Orthodontics & craniofacial research · 2008
Automated diagnosis of fetal alcohol syndrome using 3D facial image analysis.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Fang S, McLaughlin J, Fang J, Huang J, Autti-Rämö I, Fagerlund A, Jacobson S W, Robinson L K, Hoyme H E, Mattson S N, Riley E, Zhou F, Ward R, Moore E S, Foroud T, Collaborative Initiative on Fetal Alcohol Spectrum Disorders
Аннотация
OBJECTIVES: Use three-dimensional (3D) facial laser scanned images from children with fetal alcohol syndrome (FAS) and controls to develop an automated diagnosis technique that can reliably and accurately identify individuals prenatally exposed to alcohol.
METHODS: A detailed dysmorphology evaluation, history of prenatal alcohol exposure, and 3D facial laser scans were obtained from 149 individuals (86 FAS; 63 Control) recruited from two study sites (Cape Town, South Africa and Helsinki, Finland). Computer graphics, machine learning, and pattern recognition techniques were used to automatically identify a set of facial features that best discriminated individuals with FAS from controls in each sample.
RESULTS: An automated feature detection and analysis technique was developed and applied to the two study populations. A unique set of facial regions and features were identified for each population that accurately discriminated FAS and control faces without any human intervention.
CONCLUSION: Our results demonstrate that computer algorithms can be used to automatically detect facial features that can discriminate FAS and control faces.
METHODS: A detailed dysmorphology evaluation, history of prenatal alcohol exposure, and 3D facial laser scans were obtained from 149 individuals (86 FAS; 63 Control) recruited from two study sites (Cape Town, South Africa and Helsinki, Finland). Computer graphics, machine learning, and pattern recognition techniques were used to automatically identify a set of facial features that best discriminated individuals with FAS from controls in each sample.
RESULTS: An automated feature detection and analysis technique was developed and applied to the two study populations. A unique set of facial regions and features were identified for each population that accurately discriminated FAS and control faces without any human intervention.
CONCLUSION: Our results demonstrate that computer algorithms can be used to automatically detect facial features that can discriminate FAS and control faces.