Differentiating molecular etiologies of Angelman syndrome through facial phenotyping using deep learning

Facial recognition for disease diagnosis using a deep learning convolutional neural network: a systematic review and meta-analysis

BACKGROUND

With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention.

OBJECTIVE

This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software.

RESULTS

The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)].

CONCLUSION

The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.

Computer-aided facial analysis as a tool to identify patients with Silver-Russell syndrome and Prader-Willi syndrome

Genetic syndromes often show facial features that provide clues for the diagnosis. However, memorizing these features is a challenging task for clinicians. In the last years, the app Face2Gene proved to be a helpful support for the diagnosis of genetic diseases by analyzing features detected in one or more facial images of affected individuals. Our aim was to evaluate the performance of the app in patients with Silver-Russell syndrome (SRS) and Prader-Willi syndrome (PWS). We enrolled 23 pediatric patients with clinically or genetically diagnosed SRS and 29 pediatric patients with genetically confirmed PWS. One frontal photo of each patient was acquired. Top 1, top 5, and top 10 sensitivities were analyzed. Correlation with the specific genetic diagnosis was investigated. When available, photos of the same patient at different ages were compared. In the SRS group, Face2Gene showed top 1, top 5, and top 10 sensitivities of 39%, 65%, and 91%, respectively. In 41% of patients with genetically confirmed SRS, SRS was the first syndrome suggested, while in clinically diagnosed patients, SRS was suggested as top 1 in 33% of cases (p = 0.74). Face2Gene performed better in younger patients with SRS: in all patients in whom a photo taken at a younger age than the age of enrollment was available, SRS was suggested as top 1, albeit with variable degree of probability. In the PWS group, the top 1, top 5, and top 10 sensitivities were 76%, 97%, and 100%, respectively. PWS was suggested as top 1 in 83% of patients genetically diagnosed with paternal deletion of chromosome 15q11-13 and in 60% of patients presenting with maternal uniparental disomy of chromosome 15 (p = 0.17). The performance was uniform throughout the investigated age range (1-15 years).

CONCLUSION

In addition to a thorough medical history and detailed clinical examination, the Face2Gene app can be a useful tool to support clinicians in identifying children with a potential diagnosis of SRS or PWS.

WHAT IS KNOWN

• Several genetic syndromes present typical facial features that may provide clues for the diagnosis. • Memorizing all syndromic facial characteristics is a challenging task for clinicians.

WHAT IS NEW

• Face2Gene may represent a useful support for pediatricians for the diagnosis of genetic syndromes. • Face2Gene app can be a useful tool to integrate in the diagnostic path of patients with SRS and PWS.

Clinical and molecular features of 66 patients with musculocontractural Ehlers-Danlos syndrome caused by pathogenic variants in CHST14 (mcEDS-CHST14)

BACKGROUND

Musculocontractural Ehlers-Danlos syndrome is caused by biallelic loss-of-function variants in CHST14 (mcEDS-CHST14) or DSE (mcEDS-DSE). Although 48 patients in 33 families with mcEDS-CHST14 have been reported, the spectrum of pathogenic variants, accurate prevalence of various manifestations and detailed natural history have not been systematically investigated.

METHODS

We collected detailed and comprehensive clinical and molecular information regarding previously reported and newly identified patients with mcEDS-CHST14 through international collaborations.

RESULTS

Sixty-six patients in 48 families (33 males/females; 0-59 years), including 18 newly reported patients, were evaluated. Japanese was the predominant ethnicity (27 families), associated with three recurrent variants. No apparent genotype-phenotype correlation was noted. Specific craniofacial (large fontanelle with delayed closure, downslanting palpebral fissures and hypertelorism), skeletal (characteristic finger morphologies, joint hypermobility, multiple congenital contractures, progressive talipes deformities and recurrent joint dislocation), cutaneous (hyperextensibility, fine/acrogeria-like/wrinkling palmar creases and bruisability) and ocular (refractive errors) features were observed in most patients (>90%). Large subcutaneous haematomas, constipation, cryptorchidism, hypotonia and motor developmental delay were also common (>80%). Median ages at the initial episode of dislocation or large subcutaneous haematoma were both 6 years. Nine patients died; their median age was 12 years. Several features, including joint and skin characteristics (hypermobility/extensibility and fragility), were significantly more frequent in patients with mcEDS-CHST14 than in eight reported patients with mcEDS-DSE.

CONCLUSION

This first international collaborative study of mcEDS-CHST14 demonstrated that the subtype represents a multisystem disorder with unique set of clinical phenotypes consisting of multiple malformations and progressive fragility-related manifestations; these require lifelong, multidisciplinary healthcare approaches.

Investigation of a dysmorphic facial phenotype in patients with Gaucher disease types 2 and 3

Gaucher disease (GD) is a rare lysosomal storage disorder that is divided into three subtypes based on presentation of neurological manifestations. Distinguishing between the types has important implications for treatment and counseling. Yet, patients with neuronopathic forms of GD, types 2 and 3, often present at young ages and can have overlapping phenotypes. It has been shown that new technologies employing artificial intelligence and facial recognition software can assist with dysmorphology assessments. Though classically not associated nor previously described with a dysmorphic facial phenotype, this study investigated whether a facial recognition platform could distinguish between photos of patients with GD2 and GD3 and discriminate between them and photos of healthy controls. Each cohort included over 100 photos. A cross validation scheme including a series of binary comparisons between groups was used. Outputs included a composite photo of each cohort and either a receiver operating characteristic curve or a confusion matrix. Binary comparisons showed that the software could correctly group photos at least 89% of the time. Multiclass comparison between GD2, GD3, and healthy controls demonstrated a mean accuracy of 76.6%, compared to a 37.7% chance for random comparison. Both GD2 and GD3 have now been added to the facial recognition platform as established syndromes that can be identified by the algorithm. These results suggest that facial recognition and artificial intelligence, though no substitute for other diagnostic methods, may aid in the recognition of neuronopathic GD. The algorithm, in concert with other clinical features, also appears to distinguish between young patients with GD2 and GD3, suggesting that this tool can help facilitate earlier implementation of appropriate management.