Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

• The proposed methodology, visual diagnostic DSS, employs ML algorithms and image processing techniques for automated diagnosis in medical genetics.
• The proposed system was trained using a real dataset of previously published face images of subjects with syndromes.
• A high accuracy rate was achieved using this automated diagnosis technique.
• The results show that the accurate classification of syndromes is feasible using ML techniques.
• The study demonstrates the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.

BackgroundIn general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype–phenotype interrelation is possible. However, determining correct genotype–phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available.MethodsThe proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype–phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features.ResultsThe proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5–9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p < 0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively.ConclusionOur results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.