scholarly journals The artificial intelligence watcher predicts cancer risk by facial features

2022 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
HaoRan Zhang ◽  
GuangYi Lv ◽  
Shuang Liu ◽  
Dan Liu ◽  
XiongZhi Wu
Keyword(s):  
Artificial Intelligence ◽  
Cancer Risk ◽  
Facial Features

A markerless 2D video of facial features recognition based artificial intelligence model to assist Parkinson’s disease diagnosis (Preprint)

2021 ◽  
Author(s):  
Yu Zhang
Keyword(s):  
Artificial Intelligence ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Diagnostic Performance ◽  
Feature Recognition ◽  
Texture Features ◽  
Facial Feature ◽  
Disease Diagnosis ◽  
Facial Features ◽  
Geometric Features

UNSTRUCTURED Background: Mask face is a characteristic clinical manifestation of Parkinson's disease (PD), but subjective evaluations from different clinicians often show low consistency owing to lacking accurate detection technology. With the objective of making monitoring easier and more accessible, we developed a markerless 2D video of facial features recognition based artificial intelligence (AI) model to assess facial features of PD patients and aimed to investigate how AI could help neurologists improve PD early diagnostic performance. Methods: We collected 140 videos of facial expressions of 70 PD patients and 70 healthy controls from three hospitals. We developed and tested the AI model that performs mask face recognition of PD patients based on the acquisition and evaluation of facial features including geometric features and texture features, using a single 2D video camera. The diagnostic performance of AI model was compared with 5 neurologists. Results: Experimental results show that our AI models can achieve feasible and effective facial feature recognition ability to assist PD diagnosis. The precision and F1 values of PD diagnosis can reach 83% and 86%, using geometric features and texture features, respectively. When these two features are combined, a F1 value of 88% can be reached. Further, the facial features of patients with PD were not affected by the motor and non-motor symptoms of PD. Conclusions: PD patients commonly exhibit facial features. Video of facial features recognition based AI model can provide a valuable tool to assist PD diagnosis and potential of realizing remote monitoring on patients’ condition especially on the COVID-19 pandemic.

scholarly journals The ethical application of biometric facial recognition technology

AI & Society ◽  
2021 ◽  
Author(s):  
Marcus Smith ◽  
Seumas Miller
Keyword(s):  
Artificial Intelligence ◽  
Law Enforcement ◽  
Facial Recognition ◽  
Ethical Analysis ◽  
Facial Features ◽  
Democratic Accountability ◽  
Ethical Implications ◽  
Individual Privacy ◽  
Artificial Intelligence Technology ◽  
The One

AbstractBiometric facial recognition is an artificial intelligence technology involving the automated comparison of facial features, used by law enforcement to identify unknown suspects from photographs and closed circuit television. Its capability is expanding rapidly in association with artificial intelligence and has great potential to solve crime. However, it also carries significant privacy and other ethical implications that require law and regulation. This article examines the rise of biometric facial recognition, current applications and legal developments, and conducts an ethical analysis of the issues that arise. Ethical principles are applied to mediate the potential conflicts in relation to this information technology that arise between security, on the one hand, and individual privacy and autonomy, and democratic accountability, on the other. These can be used to support appropriate law and regulation for the technology as it continues to develop.

scholarly journals QUANTIFYING FACIAL EXPRESSION USING ARTIFICIAL INTELLIGENCE IN CLASS 1 MOLAR RELATIONSHIP: A NOVEL RESEARCH PROTOCOL

2021 ◽  
Vol 10 (6) ◽  
pp. 3802-3805
Author(s):  
Akshata Raut
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expressions ◽  
Class I ◽  
Facial Features ◽  
Research Protocol ◽  
Eligibility Criteria ◽  
Emotional Sensitivity ◽  
Detection Analysis ◽  
Class 1

Precise face detection analysis is a crucial element for a social interaction review. To the viewer, producing the facial features that correspond to the thoughts and feelings which succeed in arousing the sensation or enhancing of the emotional sensitivity. The study is based on Virtual Reality (VR), to evaluate facial expression using Azure Kinect in adults with Class I molar relationship. The study will be conducted in Human Research Lab, on participants with Class I molar relationship, by using Azure Kinect. 196 participants will be selected of age above 18 as per the eligibility criteria. This research would demonstrate the different tools and applications available by testing their precision and relevance to determine the facial expressions.

scholarly journals Facial Recognition Intensity in Disease Diagnosis Using Automatic Facial Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 1172
Author(s):  
Danning Wu ◽  
Shi Chen ◽  
Yuelun Zhang ◽  
Huabing Zhang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Diagnostic Accuracy ◽  
Facial Recognition ◽  
Disease Diagnosis ◽  
Facial Features ◽  
Random Effects Models ◽  
Pooled Data ◽  
Quantitative Synthesis ◽  
Meta Regression ◽  
First Time

Artificial intelligence (AI) technology is widely applied in different medical fields, including the diagnosis of various diseases on the basis of facial phenotypes, but there is no evaluation or quantitative synthesis regarding the performance of artificial intelligence. Here, for the first time, we summarized and quantitatively analyzed studies on the diagnosis of heterogeneous diseases on the basis on facial features. In pooled data from 20 systematically identified studies involving 7 single diseases and 12,557 subjects, quantitative random-effects models revealed a pooled sensitivity of 89% (95% CI 82% to 93%) and a pooled specificity of 92% (95% CI 87% to 95%). A new index, the facial recognition intensity (FRI), was established to describe the complexity of the association of diseases with facial phenotypes. Meta-regression revealed the important contribution of FRI to heterogeneous diagnostic accuracy (p = 0.021), and a similar result was found in subgroup analyses (p = 0.003). An appropriate increase in the training size and the use of deep learning models helped to improve the diagnostic accuracy for diseases with low FRI, although no statistically significant association was found between accuracy and photographic resolution, training size, AI architecture, and number of diseases. In addition, a novel hypothesis is proposed for universal rules in AI performance, providing a new idea that could be explored in other AI applications.

Application of artificial intelligence and machine learning for prediction of oral cancer risk

2021 ◽  
Author(s):  
Anwar Alhazmi ◽  
Yaser Alhazmi ◽  
Ali Makrami ◽  
Amal Masmali ◽  
Nourah Salawi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Oral Cancer ◽  
Cancer Risk ◽  
Oral Cancer Risk

Explainable health prediction from facial features with transfer learning

2021 ◽  
pp. 1-13
Author(s):  
Tee Connie ◽  
Yee Fan Tan ◽  
Michael Kah Ong Goh ◽  
Hock Woon Hon ◽  
Zulaikha Kadim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Transfer Learning ◽  
Healthcare Systems ◽  
Facial Features ◽  
Healthcare Industry ◽  
The Public ◽  
Explainable Ai ◽  
Interpretable Model ◽  
Effective Visualization

In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model.

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