A Study on the Possible Diagnosis of Parkinson’s Disease on the Basis of Facial Image Analysis

One of the symptoms of Parkinson’s disease is the occurrence of problems with the expression of emotions on the face, called facial masking, facial bradykinesia or hypomimia. Recent medical studies show that this symptom can be used in the diagnosis of this disease. In the presented study, the authors, on the basis of their own research, try to answer the question of whether it is possible to build an automatic Parkinson’s disease recognition system based on the face image. The research used image recordings in the field of visible light and infrared. The material for the study consisted of registrations in a group of patients with Parkinson’s disease and a group of healthy patients. The patients were asked to express a neutral facial expression and a smile. In the detection, both geometric and holistic methods based on the use of convolutional network and image fusion were used. The obtained results were assessed quantitatively using statistical measures, including F1score, which was a value of 0.941. The results were compared with a competitive work on the same subject. A novelty of our experiments is that patients with Parkinson’s disease were in the so-called ON phase, in which, due to the action of drugs, the symptoms of the disease are reduced. The results obtained seem to be useful in the process of early diagnosis of this disease, especially in times of remote medical examination.

Deep learning-based facial image analysis in medical research: a systematic review protocol

IntroductionDeep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people’s medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients’ welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.MethodsDatabases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study.Ethics and disseminationAs the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.PROSPERO registration numberCRD42020196473.

Pandemic Robot

Robot-based service platforms are currently establishing themselves as new and affordable variants for supporting care in elderly, retirement and nursing homes. Many are open multifunctional platforms, which can potentially be integrated into such environments, if the necessary infrastructure is available. Furthermore, many services can be realized on these platforms, which can be used to foster distant interactions between inhabitants and care-providers, while simultaneously keeping up the quality of life of the inhabitants. Open mobile robotic platforms allow the extension with adequate new sensors. To detect infectious diseases of residents and healthcare-professionals, optical sensors can be used for the assessment of vital data such as heartrate and heartrate variability, respiratory rate, SpO2 or temperature. Additionally, you can consider demographic data (age, gender, constitution) of the observed person for the optical assessment, i.e. obtained by facial image analysis. As these mobile platforms are also equipped for telepresence, in case of detecting an infected person, these systems support video conferencing with their built-in cameras and microphones. Finally, the interaction with the electronic care record is necessary to upload all acquired vital data and further relevant information. All the named technologies have been under investigation in the past years and are currently moving from laboratory settings to real-world scenarios. Nevertheless, the smooth integration of all components into one system architecture in combination with (AI-based) data analysis are still open issues.

Neural network prediction of difficult tracheal intubation risk by using the patient’s face image

Background. The prognosis of the difficult tracheal intubation remains an essential problem. The effectiveness of using predictors does not allow to foreseen such situation accurately. The purpose of the study was to develop a predictive system and evaluate its effectiveness in difficult tracheal intubation based on facial image analysis combined with the most significant predictors of difficult intubation. Materials and methods. A database based on the registration of difficult intubation predictors was developed. It was based on the patients face images with marked reference points. It allowed to estimate the information signs associated with the difficult tracheal intubation. The degree of intubation severity was determined directly during the intubation process according to the proposed original scale of severity. Results. The classifier was synthesized by using the self-organization neural network method. The trained neural network was the basis of the classifier model implemented as a computer application. The sensitivity of the difficult tracheal intubation prognosis was 90.90%, specificity was 97.02%, the prognostic value of the positive result was 58.82%, the negative one was 99.56%. Conclusions. The proposed decision support system allows patients to be stratified into groups according to the degree of difficult tracheal intubation risk. In addition, the self-learning process of the system continues as the new data become available. This allows to improve its efficiency continuously.