PDF(Pubmed)
Abstract：
UNASSIGNED: There are more than 6000 genetic syndromes, therefore the recognition of facial patterns may present a challenge for clinicians. The 22q11.2 deletion syndrome (22q11.2 DS) and Williams syndrome (WS) are two different genetic syndromes but share some common phenotypic traits and subtle facial dysmorphisms. Therefore, any tool that would help clinicians recognize genetic syndromes would likely result in a more accurate diagnosis.
UNASSIGNED: The syndrome identification accuracy was compared between 2 different facial analysis algorithms (DeepGestalt and GestaltMatcher) of the Face2Gene (F2G) tool and a group of 9 clinicians with different levels of expertise before and after using F2G for a cohort of 64 Thai participants\' frontal facial photos divided into 3 groups of 22q11.2 DS, WS and unaffected controls.
UNASSIGNED: The higher accuracy from the DeepGestalt algorithm than from clinicians was demonstrated, especially when comparing between the two syndromes. The accuracy was highest when clinicians use the tool combined with their own decision-making process. The tool\'s second algorithm, GestaltMatcher revealed clear separation among these three groups of photos.
UNASSIGNED: The result of F2G outperforming clinicians was not surprising. However, the highest increase in accuracy was with nondysmorphology clinicians using F2G.
UNASSIGNED: Face2Gene would be a useful tool to help clinicians in facial recognition of genetic syndromes, before ordering specific tests to confirm the definite diagnosis.
UNASSIGNED: The syndrome identification accuracy was compared between 2 different facial analysis algorithms (DeepGestalt and GestaltMatcher) of the Face2Gene (F2G) tool and a group of 9 clinicians with different levels of expertise before and after using F2G for a cohort of 64 Thai participants\' frontal facial photos divided into 3 groups of 22q11.2 DS, WS and unaffected controls.
UNASSIGNED: The higher accuracy from the DeepGestalt algorithm than from clinicians was demonstrated, especially when comparing between the two syndromes. The accuracy was highest when clinicians use the tool combined with their own decision-making process. The tool\'s second algorithm, GestaltMatcher revealed clear separation among these three groups of photos.
UNASSIGNED: The result of F2G outperforming clinicians was not surprising. However, the highest increase in accuracy was with nondysmorphology clinicians using F2G.
UNASSIGNED: Face2Gene would be a useful tool to help clinicians in facial recognition of genetic syndromes, before ordering specific tests to confirm the definite diagnosis.
摘要：
■有6000多个遗传综合征，因此，面部模式的识别可能给临床医生带来挑战。22q11.2缺失综合征（22q11.2DS）和威廉姆斯综合征（WS）是两种不同的遗传综合征，但具有一些共同的表型特征和微妙的面部畸形。因此,任何有助于临床医生识别遗传综合征的工具都可能导致更准确的诊断。
■在Face2Gene（F2G）工具的2种不同的面部分析算法（DeepGestalt和GestaltMatcher）和一组9名具有不同专业知识水平的临床医生之前和之后，对64名泰国参与者的正面面部照片进行了比较，分为3组22q11.2DS，WS和不受影响的控制。
■已证明DeepGestalt算法的准确性高于临床医生，特别是在比较两种综合症时。当临床医生结合自己的决策过程使用该工具时，准确性最高。该工具的第二个算法，GestaltMatcher揭示了这三组照片之间的清晰分离。
■F2G的结果优于临床医生并不令人惊讶。然而,准确性提高最高的是使用F2G的非形态学临床医生.
■Face2Gene将是帮助临床医生识别遗传综合征的有用工具，在订购特定测试以确认明确诊断之前。
■在Face2Gene（F2G）工具的2种不同的面部分析算法（DeepGestalt和GestaltMatcher）和一组9名具有不同专业知识水平的临床医生之前和之后，对64名泰国参与者的正面面部照片进行了比较，分为3组22q11.2DS，WS和不受影响的控制。
■已证明DeepGestalt算法的准确性高于临床医生，特别是在比较两种综合症时。当临床医生结合自己的决策过程使用该工具时，准确性最高。该工具的第二个算法，GestaltMatcher揭示了这三组照片之间的清晰分离。
■F2G的结果优于临床医生并不令人惊讶。然而,准确性提高最高的是使用F2G的非形态学临床医生.
■Face2Gene将是帮助临床医生识别遗传综合征的有用工具，在订购特定测试以确认明确诊断之前。
