Multiple facial image features-based recognition for the automatic diagnosis of turner syndrome

Artificial intelligence in paediatric endocrinology: conflict or cooperation

Artificial intelligence (AI) in medicine is transforming healthcare by automating system tasks, assisting in diagnostics, predicting patient outcomes and personalising patient care, founded on the ability to analyse vast datasets. In paediatric endocrinology, AI has been developed for diabetes, for insulin dose adjustment, detection of hypoglycaemia and retinopathy screening; bone age assessment and thyroid nodule screening; the identification of growth disorders; the diagnosis of precocious puberty; and the use of facial recognition algorithms in conditions such as Cushing syndrome, acromegaly, congenital adrenal hyperplasia and Turner syndrome. AI can also predict those most at risk from childhood obesity by stratifying future interventions to modify lifestyle. AI will facilitate personalised healthcare by integrating data from 'omics' analysis, lifestyle tracking, medical history, laboratory and imaging, therapy response and treatment adherence from multiple sources. As data acquisition and processing becomes fundamental, data privacy and protecting children's health data is crucial. Minimising algorithmic bias generated by AI analysis for rare conditions seen in paediatric endocrinology is an important determinant of AI validity in clinical practice. AI cannot create the patient-doctor relationship or assess the wider holistic determinants of care. Children have individual needs and vulnerabilities and are considered in the context of family relationships and dynamics. Importantly, whilst AI provides value through augmenting efficiency and accuracy, it must not be used to replace clinical skills.

A novel energy consumption prediction method for chillers based on an improved support vector machine

The energy consumption prediction of the chiller is an important means to reduce the energy consumption of buildings. Therefore, a novel energy consumption prediction model for chillers based on an improved support vector machine (ICA-DE-SVM) is proposed. The imperialist competitive algorithm (ICA) is used to optimize the penalty coefficient and kernel function width of SVM, greatly improving the generalization ability and prediction accuracy of the SVM model. The assimilation process is very important in ICA. Colonies of empires move randomly toward imperialists during the assimilation process in ICA, which decreases population diversity and can lead to premature convergence. Therefore, to create more new locations for colonies and increase population diversity, the idea of differential mutation proposed by differential evolution (DE) was applied to ICA. The established model was experimentally verified in an actual multi-chiller system in a building, and the results showed that the ICA-DE-SVM model could obtain good prediction results. Finally, the proposed model was compared with SVM model, PSO-SVM model, GA-SVM model, WOA-SVM model, and ICA-SVM model. With an MAPE of 0.6%, an MSE of 2.3, and an R 2 of 0.9998, the findings demonstrate that the ICA-DE-SVM model has a greater prediction accuracy than the other models.

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.

Static and Motion Facial Analysis for Craniofacial Assessment and Diagnosing Diseases

Deviation from a normal facial shape and symmetry can arise from numerous sources, including physical injury and congenital birth defects. Such abnormalities can have important aesthetic and functional consequences. Furthermore, in clinical genetics distinctive facial appearances are often associated with clinical or genetic diagnoses; the recognition of a characteristic facial appearance can substantially narrow the search space of potential diagnoses for the clinician. Unusual patterns of facial movement and expression can indicate disturbances to normal mechanical functioning or emotional affect. Computational analyses of static and moving 2D and 3D images can serve clinicians and researchers by detecting and describing facial structural, mechanical, and affective abnormalities objectively. In this review we survey traditional and emerging methods of facial analysis, including statistical shape modeling, syndrome classification, modeling clinical face phenotype spaces, and analysis of facial motion and affect. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Clinical application of an automatic facial recognition system based on deep learning for diagnosis of Turner syndrome

PURPOSE

Automated facial recognition technology based on deep learning has achieved high accuracy in diagnosing various endocrine diseases and genetic syndromes. This study attempts to establish a facial diagnostic system for Turner syndrome (TS) based on deep convolutional neural networks.

METHODS

Photographs of 207 TS patients and 1074 female controls were collected from July 2016 to April 2019. Finally, 170 patients diagnosed with TS and 1053 female controls were included. Deep convolutional neural networks were used to develop the facial diagnostic system. A prospective study, which included two TS patients and 35 controls, was conducted to test the efficacy in the real clinical setting.

RESULTS

The average areas under the curve (AUCs) in three different scenarios were 0.9540 ± 0.0223, 0.9662 ± 0.0108 and 0.9557 ± 0.0119, separately. The average sensitivity and specificity of the prospective study were 96.7% and 97.0%, respectively.

CONCLUSIONS

The facial diagnostic system achieved high accuracy. Prospective study results demonstrated the application value of this system, which is promising in the screening of Turner syndrome.

A Building Extraction Approach Based on the Fusion of LiDAR Point Cloud and Elevation Map Texture Features

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.