Plastic and reconstructive surgery · 2024
Diagnosis and Screening of Velocardiofacial Syndrome by Evaluating Facial Photographs Using a Deep Learning-Based Algorithm.
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Baek Rong-Min, Cho Anna, Chung Yoon Gi, Jeon Yonghoon, Kim Hunmin, Hwang Hee, Kang Jiwon, Myung Yujin
Аннотация
BACKGROUND: Early detection of rare genetic diseases, including velocardiofacial syndrome (VCFS), is essential for patient well-being. However, the rarity of these diseases and limited clinical experience of physicians make diagnosis challenging. Deep learning algorithms have emerged as promising tools for efficient and accurate diagnosis. This study investigates the use of a deep learning algorithm to develop a face recognition model for diagnosing VCFS.
METHODS: The study used publicly available labeled face data sets to train the multitask cascaded convolutional neural networks model. Subsequently, the authors examined the binary classification performance for diagnosing VCFS using the most efficient face recognition model. A total of 98 VCFS patients (920 facial photographs) and 91 non-VCFS controls (463 facial photographs) were randomly divided into training and test sets. In addition, the authors analyzed whether the classification results matched the known facial phenotype of VCFS.
RESULTS: The face recognition model demonstrated high accuracy, ranging from 94% to 99%, depending on the training data set. The accuracy of the binary classification diagnostic model varied from 81% to 88% when evaluating with photographs taken at various angles, but reached 95% evaluating with frontal photographs only. Gradient-weighted class activation mapping heat map revealed the high importance level of perinasal and periorbital areas, exhibiting consistency with the conventional facial phenotypes of VCFS.
CONCLUSIONS: This study shows the feasibility and effectiveness of multitask cascaded convolutional neural network-based model for detecting VCFS solely from facial photographs. The high accuracy underscores the potential of deep learning in aiding early diagnosis of rare genetic diseases, facilitating timely interventions for patient care.
METHODS: The study used publicly available labeled face data sets to train the multitask cascaded convolutional neural networks model. Subsequently, the authors examined the binary classification performance for diagnosing VCFS using the most efficient face recognition model. A total of 98 VCFS patients (920 facial photographs) and 91 non-VCFS controls (463 facial photographs) were randomly divided into training and test sets. In addition, the authors analyzed whether the classification results matched the known facial phenotype of VCFS.
RESULTS: The face recognition model demonstrated high accuracy, ranging from 94% to 99%, depending on the training data set. The accuracy of the binary classification diagnostic model varied from 81% to 88% when evaluating with photographs taken at various angles, but reached 95% evaluating with frontal photographs only. Gradient-weighted class activation mapping heat map revealed the high importance level of perinasal and periorbital areas, exhibiting consistency with the conventional facial phenotypes of VCFS.
CONCLUSIONS: This study shows the feasibility and effectiveness of multitask cascaded convolutional neural network-based model for detecting VCFS solely from facial photographs. The high accuracy underscores the potential of deep learning in aiding early diagnosis of rare genetic diseases, facilitating timely interventions for patient care.