In 1896, Antoine Marfan first reported the syndrome that bears his name in the Bulletin of the Medical Society of Paris. He described the physical features of Gabrielle, a six-year-old girl with long, thin extremities. It has since been questioned whether that child actually suffered from Marfan syndrome or from a related disease (congenital contractural arachnodactyly).
For the ensuing years, the diagnosis of Marfan's disease has been predicated on clinical judgment, based on a variety of physical features. “Experts” felt that they could identify Marfan's disease with a glance and confirm the diagnosis upon closer overall clinical evaluation. In 1996, the Ghent Nosology for clinical diagnosis of Marfan's Disease was articulated . This advance identified specific features in various organ systems, which were then graded to yield numerical confirmation of the diagnosis of Marfan's disease.
Marfan syndrome has an incidence of approximately 1 in 3000–5000 human beings .
Caused by mutations in the FBN1 gene responsible for fibrillin-1 production, a protein essential to connective tissue, Marfan Syndrome exhibits a broad phenotypic range . Recognizable physical features include disproportionately long limbs, arachnodactyly (long fingers and toes), tall stature, and distinct facial features like malar hypoplasia (underdeveloped cheekbones), dolichocephaly (elongation of the head), down-slanting palpebral fissures (elliptical opening between the two eyelids slants downward laterally), and retrognathia (recessed lower jaw). These unique physical manifestations present an opportunity to explore non-invasive diagnostic methods, such as facial image analysis.
In recent years, Artificial Intelligence (AI) has made a dramatic impact in clinical medicine . For example, at many medical centers, the diagnosis of aortic dissection is first made by AI . When AI reads a computerized tomographic scan (CT) as showing an aortic dissection, an urgent message is sent electronically to a battery of key team members—often before a radiologist has even seen the images. Via that notification, the operating room team can be mobilized for immediate surgical intervention. It has been shown that the accuracy of AI in this diagnosis (aortic dissection) is extremely high. Humans cannot be sure what features AI is using in making its immediate diagnosis of aortic dissection.
Some examples of the broad applicability of AI in general, and CNNs specifically, in medical imaging include: AiDoc – a growing ecosystem of AI-enabled tools, currently encompassing diagnosis and management of several cardiovascular, neurologic, and radiology applications; AliveCo- AliveCor has received FDA clearance for the use of AI to interpret ECGs to make determinations of multiple cardiac conditions, including sinus rhythm with premature ventricular contractions (PVCs), sinus rhythm with supraventricular ectopy (SVE), and sinus rhythm with wide QRS; Face2Gene – a suite of phenotyping applications that facilitate comprehensive and precise genetic evaluations.
Convolutional Neural Networks (CNNs) are a type of deep learning model that excels in image analysis and recognition tasks . Unlike traditional machine learning models, CNNs autonomously learn hierarchical representations from raw input data, eliminating the need for manual feature extraction. They consist of multiple layers, including convolutional layers for feature extraction, pooling layers for down-sampling data, and fully connected layers for final output predictions. CNNs have been effectively employed in a broad spectrum of applications, from autonomous vehicles to medical imaging diagnostics, showcasing their robust versatility .
We wondered if AI could accurately make the diagnosis of Marfan's disease based on facial features alone. We report herein our findings on this question.
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