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

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

Editorial: Statistical model-based computational biomechanics: applications in joints and internal organs

Peer reviewed: Tru

Confronting historical legacies of biological anthropology in South Africa-Restitution, redress and community-centered science: The Sutherland Nine.

We describe a process of restitution of nine unethically acquired human skeletons to their families, together with attempts at redress. Between 1925-1927 C.E., the skeletonised remains of nine San or Khoekhoe people, eight of them known-in-life, were removed from their graves on the farm Kruisrivier, near Sutherland in the Northern Cape Province of South Africa. They were donated to the Anatomy Department at the University of Cape Town. This was done without the knowledge or permission of their families. The donor was a medical student who removed the remains from the labourers' cemetery on his family farm. Nearly 100 years later, the remains are being returned to their community, accompanied by a range of community-driven interdisciplinary historical, archaeological and analytical (osteobiographic, craniofacial, ancient DNA, stable isotope) studies to document, as far as possible, their lives and deaths. The restitution process began by contacting families living in the same area with the same surnames as the deceased. The restitution and redress process prioritises the descendant families' memories, wishes and desire to understand the situation, and learn more about their ancestors. The descendant families have described the process as helping them to reconnect with their ancestors. A richer appreciation of their ancestors' lives, gained in part from scientific analyses, culminating with reburial, is hoped to aid the descendant families and wider community in [re-]connecting with their heritage and culture, and contribute to restorative justice, reconciliation and healing while confronting a traumatic historical moment. While these nine individuals were exhumed as specimens, they will be reburied as people