TY - JOUR
T1 - Evaluation of a computer-based facial dysmorphology analysis algorithm (Face2Gene) using standardized textbook photos
AU - Javitt, Matthew J.
AU - Vanner, Elizabeth A.
AU - Grajewski, Alana L.
AU - Chang, Ta C.
N1 - Funding Information:
We wish to acknowledge Mr. Manuel Grosskopf & Family for their generous support of the Samuel & Ethel Balkan International Pediatric Glaucoma Center, and their tremendous foresight in the value of artificial intelligence clinical research.
Funding Information:
Funding The project was supported by the National Institute of Health (NIH) Center Core Grant P30EY014801, Research to Prevent Blindness Unrestricted Grant, The 2019 University of Miami Institute for Advanced Study of the Americas Pilot Grant, 2018 IRIS-Registry-AGS Research Initiative Grant, and Grant Number UL1TR002736, Miami Clinical and Translational Science Institute, from the National Center for Advancing Translational Sciences and the National Institute on Minority Health and Health Disparities. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to The Royal College of Ophthalmologists.
PY - 2022/4
Y1 - 2022/4
N2 - Background: Genetic syndromes often have ocular involvement. Ophthalmologists may have difficulty identifying dysmorphic features in genetic syndrome evaluations. We investigated the sensitivity and specificity of Face2Gene (F2G), a digital image analysis software trained on integrating dysmorphic features, by analysing patient photos from genetics textbooks. Methods: We analysed all clear facial photos contained within the textbooks Smith’s Recognizable Patterns of Human Malformation and Genetic Diseases of the Eye using F2G under standard lighting conditions. Variables captured include colour versus grey scale photo, the gender of the patient (if known), age of the patient (if known), disease categories, diagnosis as listed in the textbook, and whether the disease has ophthalmic involvement (as described in the textbook entries). Any photos rejected by F2G were excluded. We analysed the data for accuracy, sensitivity, and specificity based on disease categories as outlined in Smith’s Recognizable Patterns of Malformation. Results: We analysed 353 photos found within two textbooks. The exact book diagnosis was identified by F2G in 150 (42.5%) entries, and was included in the top three differential diagnoses in 191 (54.1%) entries. F2G is highly sensitive for craniosynostosis syndromes (point estimate [PE] 80.0%, 95% confidence interval [CI] 56.3–94.3%, P = 0.0118) and syndromes with facial defects as a major feature (PE 77.8%, 95% CI 52.4–93.6%, P = 0.0309). F2G was highly specific (PE > 83percentage with P < 0.001) for all disease categories. Conclusions: F2G is a useful tool for paediatric ophthalmologists to help build a differential diagnosis when evaluating children with dysmorphic facial features.
AB - Background: Genetic syndromes often have ocular involvement. Ophthalmologists may have difficulty identifying dysmorphic features in genetic syndrome evaluations. We investigated the sensitivity and specificity of Face2Gene (F2G), a digital image analysis software trained on integrating dysmorphic features, by analysing patient photos from genetics textbooks. Methods: We analysed all clear facial photos contained within the textbooks Smith’s Recognizable Patterns of Human Malformation and Genetic Diseases of the Eye using F2G under standard lighting conditions. Variables captured include colour versus grey scale photo, the gender of the patient (if known), age of the patient (if known), disease categories, diagnosis as listed in the textbook, and whether the disease has ophthalmic involvement (as described in the textbook entries). Any photos rejected by F2G were excluded. We analysed the data for accuracy, sensitivity, and specificity based on disease categories as outlined in Smith’s Recognizable Patterns of Malformation. Results: We analysed 353 photos found within two textbooks. The exact book diagnosis was identified by F2G in 150 (42.5%) entries, and was included in the top three differential diagnoses in 191 (54.1%) entries. F2G is highly sensitive for craniosynostosis syndromes (point estimate [PE] 80.0%, 95% confidence interval [CI] 56.3–94.3%, P = 0.0118) and syndromes with facial defects as a major feature (PE 77.8%, 95% CI 52.4–93.6%, P = 0.0309). F2G was highly specific (PE > 83percentage with P < 0.001) for all disease categories. Conclusions: F2G is a useful tool for paediatric ophthalmologists to help build a differential diagnosis when evaluating children with dysmorphic facial features.
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U2 - 10.1038/s41433-021-01563-5
DO - 10.1038/s41433-021-01563-5
M3 - Article
C2 - 33931761
AN - SCOPUS:85105126978
VL - 36
SP - 859
EP - 861
JO - Transactions of the Ophthalmological Societies of the United Kingdom
JF - Transactions of the Ophthalmological Societies of the United Kingdom
SN - 0950-222X
IS - 4
ER -