Statistical analysis of facial landmark data for optimisation of Fetal Alcohol Syndrome diagnosis

Includes bibliographical references (leaves 100-104).This project involved the statistical analysis of facial landmark used in Fetal Alcohol Syndrome (FAS) diagnosis. FAS is a clinical condition caused by excessive maternal consumption of alcohol during pregnancy. Diagnosis of FAS depends on evidence of growth retardation, CNS neurodevelopment abnormalities, and a characteristic pattern of facial anomalies, specifically a short palpebral fissure length, smooth philtrum, flat upper lip and flat midface. The unique facial appearance associated with FAS is emphasized in diagnosis that relies, in part, on the comparison of linear measurements of facial features to population norms

Characterization of the facial phenotype associated with fetal alcohol syndrome using stereo-photogrammetry and geometric morphometrics

Includes abstract.Includes bibliographical references (leaves 108-118).Fetal Alcohol Syndrome (FAS) is a clinical condition caused by excessive pre-natal alcohol exposure and is regarded as a leading identifiable and preventable cause of mental retardation in the Western world. The highest prevalence of FAS was reported in the wine-growing regions of South Africa but data for the rest of the country is not available. Required, therefore, are large-scale screening and surveillance programmes to be conducted in South Africa in order for the epidemiology of the disease to be understood. Efforts to this end have been stymied by the cost and labour-intensive nature of collecting the facial anthropometric data useful in FAS diagnosis. Stereo-photogrammetry provides a low cost, easy to use and non-invasive alternative to traditional facial anthropometry. The design and implementation of a landmark-based stereo-photogrammetry system to obtain 3D facial information for fetal alcohol syndrome diagnosis (FAS) is described. The system consists of three high resolution digital cameras resting on a purpose-built stand and a control frame which surrounds the subject's head during imaging. Reliability and assessments of accuracy for the stereo-photogrammetric tool are presented using 275 inter-landmark distance comparisons between the system and direct anthropometry using a doll. These showed the system to be highly reliable and precise

Machine learning as an enabler of medical technology

Driven by advancements in digital computing, data storage, and the availability of large datasets from digitized healthcare workflows and telemedicine, machine learning is swiftly becoming integral to the most diverse aspects of medical technology. It's not merely about optimizing complex clinical tasks; it's also about fostering innovative applications such as large-scale image screening, data inference, and automatic diagnostics. Indeed, machine learning is a prerequisite for a radically new approach to these tasks, transcending the re-implementation of established technologies. This special issue spotlights papers where machine learning is an essential constituent of medical technology innovation

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research

Validation d'un Nouveau Modèle Statistique de Scapula Augmenté de Marqueurs Anatomiques

International audienceCe papier décrit la validation d'un modèle statistique de scapula (SSM) augmenté d'un ensemble de marqueurs anatomiques ayant un intérêt clinique. Le SSM utilisé est issu de nos récents travaux ayant abouti à la publication d'un des premiers modèles statistiques de l'os scapulaire chez l'humain adulte. En effet, la scapula est une forme 3D difficile à modéliser statistiquement du fait de sa forme complexe et de sa grande variabilité. Ce SSM avait été validé par les critères classiques de robustesse de construction du SSM à savoir, compacité, généralité et spécificité. Cependant, la robustesse de la représentation statistique n'est pas garante de sa validité anatomique pourtant primordiale pour des applications cliniques. Dans cette étude, nous présentons une nouvelle méthode pour l'ajout d'informations anatomiques dans le SSM développé et nous l'évaluons par un processus de sélection des marqueurs anatomiques utilisant un groupe mixte d'observateurs. Nous obtenons d'excellents résultats issus des analyses de variance intra et inter-observateurs. Ces résultats nous permettent d'envisager l'utilisation de ce SSM augmenté pour des applications de segmentation automatique d'IRM et des études biomécaniques du complexe de l'épaule

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Evaluating 3D human face reconstruction from a frontal 2D image, focusing on facial regions associated with foetal alcohol syndrome

Foetal alcohol syndrome (FAS) is a preventable condition caused by maternal alcohol consumption during pregnancy. The FAS facial phenotype is an important factor for diagnosis, alongside central nervous system impairments and growth abnormalities. Current methods for analysing the FAS facial phenotype rely on 3D facial image data, obtained from costly and complex surface scanning devices. An alternative is to use 2D images, which are easy to acquire with a digital camera or smart phone. However, 2D images lack the geometric accuracy required for accurate facial shape analysis. Our research offers a solution through the reconstruction of 3D human faces from single or multiple 2D images. We have developed a framework for evaluating 3D human face reconstruction from a single-input 2D image using a 3D face model for potential use in FAS assessment. We first built a generative morphable model of the face from a database of registered 3D face scans with diverse skin tones. Then we applied this model to reconstruct 3D face surfaces from single frontal images using a model-driven sampling algorithm. The accuracy of the predicted 3D face shapes was evaluated in terms of surface reconstruction error and the accuracy of FAS-relevant landmark locations and distances. Results show an average root mean square error of 2.62 mm. Our framework has the potential to estimate 3D landmark positions for parts of the face associated with the FAS facial phenotype. Future work aims to improve on the accuracy and adapt the approach for use in clinical settings. Significance: Our study presents a framework for constructing and evaluating a 3D face model from 2D face scans and evaluating the accuracy of 3D face shape predictions from single images. The results indicate low generalisation error and comparability to other studies. The reconstructions also provide insight into specific regions of the face relevant to FAS diagnosis. The proposed approach presents a potential cost-effective and easily accessible imaging tool for FAS screening, yet its clinical application needs further research

An automated statistical shape model developmental pipeline: implications to shoulder surgery parameter

International audienceUsing Statistical Shape Models (SSM) of human scapula (S) and humerus (H) in evaluating surgical parameters can lead to successful outcomes. This work presents an integrated pipeline for building an automated and unbiased global SSM of these bones from a set of CT scans (Sn = 27, Hn = 28). First, an intrinsic consensus shape was established using an Iterative Median Closest Point algorithm (groupwise rigid registration), eliminating the need for manual landmarking/region building that induce bias. Then a mean-virtual (Mv) shape was developed using a Coherence Point Drift method (non-rigid registration). This Mv shape was used to identically resample each of the original datasets with one-to-one correspondences through the basis (Mv estimates). SSM of S and H was derived by conducting a probabilistic Principal Component Analysis on Mv estimates using Statismo toolkit. This method was compared with 1) Expectation Maximization-Iterative Closest Point algorithm, and 2) groupwise Gaussian mixture model based registration on hippocampi data (n = 42) and performed equal to or better than these two methods based on generality, specificity and compactness criteria