Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application

Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development

BACKGROUND

Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.

OBJECTIVE

This study aimed to investigate the application of facial recognition technology in the early detection of iGCTs in children and adolescents. Early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.

METHODS

A multicenter, phased approach was adopted for the development and validation of a deep learning model, GVisageNet, dedicated to the screening of midline brain tumors from normal controls (NCs) and iGCTs from other midline brain tumors. The study comprised the collection and division of datasets into training (n=847, iGCTs=358, NCs=300, other midline brain tumors=189) and testing (n=212, iGCTs=79, NCs=70, other midline brain tumors=63), with an additional independent validation dataset (n=336, iGCTs=130, NCs=100, other midline brain tumors=106) sourced from 4 medical institutions. A regression model using clinically relevant, statistically significant data was developed and combined with GVisageNet outputs to create a hybrid model. This integration sought to assess the incremental value of clinical data. The model's predictive mechanisms were explored through correlation analyses with endocrine indicators and stratified evaluations based on the degree of hypothalamic-pituitary-target axis damage. Performance metrics included area under the curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

On the independent validation dataset, GVisageNet achieved an AUC of 0.938 (P<.01) in distinguishing midline brain tumors from NCs. Further, GVisageNet demonstrated significant diagnostic capability in distinguishing iGCTs from the other midline brain tumors, achieving an AUC of 0.739, which is superior to the regression model alone (AUC=0.632, P<.001) but less than the hybrid model (AUC=0.789, P=.04). Significant correlations were found between the GVisageNet's outputs and 7 endocrine indicators. Performance varied with hypothalamic-pituitary-target axis damage, indicating a further understanding of the working mechanism of GVisageNet.

CONCLUSIONS

GVisageNet, capable of high accuracy both independently and with clinical data, shows substantial potential for early iGCTs detection, highlighting the importance of combining deep learning with clinical insights for personalized health care.

Comparison of the Accuracy in Provisional Diagnosis of 22q11.2 Deletion and Williams Syndromes by Facial Photos in Thai Population Between De-Identified Facial Program and Clinicians

Introduction

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.

Methods

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.

Results

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.

Discussion

The result of F2G outperforming clinicians was not surprising. However, the highest increase in accuracy was with nondysmorphology clinicians using F2G.

Conclusion

Face2Gene would be a useful tool to help clinicians in facial recognition of genetic syndromes, before ordering specific tests to confirm the definite diagnosis.

Photoanthropometric study: are non-professional photographs suitable for objective and reliable analysis of facial features?

BACKGROUND

The face has been widely investigated using professionally taken frontal and lateral photographs, however, there is a lack of studies of non-professional facial photographs. It is not known if they could be suitable for facial analysis. The analysis of non-professional photographs could allow the performance of cost- effective longitudinal studies.

AIM

To determine if non-professional photographs could be used for a reliable analysis of facial features.

SUBJECTS AND METHODS

The frontal profiles of 18-21-year-olds (35 males, 39 females) were measured by direct anthropometry, in addition, professional photographs were taken and non-professional photographs were obtained. Anthropometric landmarks were superimposed on those photographs. The indices calculated on the basis of the measurements of direct anthropometry and both types of photographs were compared.

RESULTS

The comparison of the measurements of direct anthropometry and professional photographs showed no difference between 14 out of 25 male and 10 out of 25 female facial indices (p > 0.05) after comparing the results of direct anthropometry with those of non-professional photographs, no difference was found in 8 out of 25 male and 7 out of 25 female indices. These indices were mostly composed of vertical parameters and eye measurements.

CONCLUSION

Vertical facial dimensions and eye measurements may not only be used interchangeably for both facial photographs and direct anthropometry, but may also be suitable for objective and reliable facial analyses.

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

Machine learning based analysis for intellectual disability in Down syndrome

Down syndrome (DS) or trisomy 21 is the most common genetic cause of intellectual disability (ID), but a pathogenic mechanism has not been identified yet. Studying a complex and not monogenic condition such as DS, a clear correlation between cause and effect might be difficult to find through classical analysis methods, thus different approaches need to be used. The increased availability of big data has made the use of artificial intelligence (AI) and in particular machine learning (ML) in the medical field possible. The purpose of this work is the application of ML techniques to provide an analysis of clinical records obtained from subjects with DS and study their association with ID. We have applied two tree-based ML models (random forest and gradient boosting machine) to the research question: how to identify key features likely associated with ID in DS. We analyzed 109 features (or variables) in 106 DS subjects. The outcome of the analysis was the age equivalent (AE) score as indicator of intellectual functioning, impaired in ID. We applied several methods to configure the models: feature selection through Boruta framework to minimize random correlation; data augmentation to overcome the issue of a small dataset; age effect mitigation to take into account the chronological age of the subjects. The results show that ML algorithms can be applied with good accuracy to identify variables likely involved in cognitive impairment in DS. In particular, we show how random forest and gradient boosting machine produce results with low error (MSE <0.12) and an acceptable R2 (0.70 and 0.93). Interestingly, the ranking of the variables point to several features of interest related to hearing, gastrointestinal alterations, thyroid state, immune system and vitamin B12 that can be considered with particular attention for improving care pathways for people with DS. In conclusion, ML-based model may assist researchers in identifying key features likely correlated with ID in DS, and ultimately, may improve research efforts focused on the identification of possible therapeutic targets and new care pathways. We believe this study can be the basis for further testing/validating of our algorithms with multiple and larger datasets.

Review on Facial-Recognition-Based Applications in Disease Diagnosis

Diseases not only manifest as internal structural and functional abnormalities, but also have facial characteristics and appearance deformities. Specific facial phenotypes are potential diagnostic markers, especially for endocrine and metabolic syndromes, genetic disorders, facial neuromuscular diseases, etc. The technology of facial recognition (FR) has been developed for more than a half century, but research in automated identification applied in clinical medicine has exploded only in the last decade. Artificial-intelligence-based FR has been found to have superior performance in diagnosis of diseases. This interdisciplinary field is promising for the optimization of the screening and diagnosis process and assisting in clinical evaluation and decision-making. However, only a few instances have been translated to practical use, and there is need of an overview for integration and future perspectives. This review mainly focuses on the leading edge of technology and applications in varieties of disease, and discusses implications for further exploration.