Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality

Nadimi, Esmaeil S.; Majtner, Tomáš; Yderstræde, Knud Bonnet; Blanes-Vidal, Victoria

doi:10.1038/s41598-020-73744-3

Cited by 3 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall accuracy of our suggested model is shown in Figure 10 . In comparison to [ 18 , 25 , 30 , 31 ] and [ 29 ], our model produces the highest accuracy. In Bell's palsy, the facial nerve passes through a narrow bone corridor that paralyzes the facial region.…”

Section: Resultsmentioning

confidence: 95%

“…Vitiligo occurs as depigmentation of the skin due to increasing blood flow. Random matrix theory (RM) [ 25 ] does not apply to all skin pattern changes, such as a network of nerves, the nerves in the corridor of bone and increasing blood flow in a particular area of nerve because of its complex analysis, such as eigenvalues, eigenvectors, and spectral analysis. The assumptions of statistical properties for analyzing random matrices are independent of matrix elements.…”

Section: Resultsmentioning

confidence: 99%

“…Histologically, diabetic individuals' cheeks have an increase in the number and width of superficial venules and butterfly-shaped cheek redness. The cheek's redness is identified automatically through temporal facial expression [ 25 ], histogram [ 26 ], and geometric transformation [ 18 ]. The most frequent cause of lower motor neuron facial palsy is Bell's palsy (BP) [ 27 ], an unpredictable, severe central facial nerve disorder.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region

Euprazia,

Rajeswari,

Thyagharajan

et al. 2023

Journal of Diabetes Research

View full text Add to dashboard Cite

Aim. Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Materials and Method. In this study, 200 facial images of diabetes patients with skin conditions such as Bell’s palsy, rubeosis faciei, scleroderma, and vitiligo were collected from existing face videos. Moreover, face images are collected from diabetic persons in India. Viola Jones’ face-detecting algorithm extracts face skin regions from a diabetic person’s face image in video frames. The affected skin area on the diabetic person’s face is detected using HSV colour model segmentation. The proposed multiwavelet transform convolutional neural network (MWTCNN) extracts the features for diabetic measurement from up- and downfacial scaled images of diabetic persons. Results. The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. Conclusion. The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.

show abstract

Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region

Euprazia,

Rajeswari,

Thyagharajan

et al. 2023

Journal of Diabetes Research

View full text Add to dashboard Cite

show abstract

“…We focus on epilepsy as a paradigmatic case, but similar approaches may be followed in other subfields of neurology: for example, considerations of assistive/autonomous usage, locked/continuous learning, and potential for self-fulfilling prophecy in automated detection of stroke and intracerebral hemorrhage 37 ; explicability and patient privacy in deep learning to predict Alzheimer disease 38 ; and patient privacy and latent bias in AI-based systems to predict diabetic neuropathy using facial recognition from home cameras. 39 Adopting a systematic approach to considering the ethical ramifications of emerging research on the principles of beneficence, nonmaleficence, patient autonomy, justice, and explicability can help ensure that the patient's benefit remains at the forefront of the neurologic community's efforts.…”

Section: Discussionmentioning

confidence: 99%

Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology

et al. 2021

View full text Add to dashboard Cite

Pre-emptive recognition of the ethical implications of study design and algorithm choices in artificial intelligence (AI) research is an important but challenging process. AI applications have begun to transition from a promising future to clinical reality in neurology. As the clinical management of neurology is often concerned with discrete, often unpredictable, and highly consequential events linked to multimodal data streams over long timescales, forthcoming advances in AI have great potential to transform care for patients. However, critical ethical questions have been raised with implementation of the first AI applications in clinical practice. Clearly, AI will have far-reaching potential to promote, but also to endanger, ethical clinical practice. This article employs an anticipatory ethics approach to scrutinize how researchers in neurology can methodically identify ethical ramifications of design choices early in the research and development process, with a goal of pre-empting unintended consequences that may violate principles of ethical clinical care. First, we discuss the use of a systematic framework for researchers to identify ethical ramifications of various study design and algorithm choices. Second, using epilepsy as a paradigmatic example, anticipatory clinical scenarios that illustrate unintended ethical consequences are discussed, and failure points in each scenario evaluated. Third, we provide practical recommendations for understanding and addressing ethical ramifications early in methods development stages. Awareness of the ethical implications of study design and algorithm choices that may unintentionally enter AI is crucial to ensuring that incorporation of AI into neurology care leads to patient benefit rather than harm.

show abstract

“…is algorithm does not need to predict the statistical characteristics and noise variance of data noise but uses the limit distribution characteristics of random matrix eigenvalues for perception [16]. Ayumi proposed a competency estimation method suitable for limited data.…”

Section: Related Workmentioning

confidence: 99%

Evaluation and Analysis of English Teaching Ability Based on Nonlinear Random Matrix Model

Zhou

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

With the sustained, rapid, and steady development of China’s economy, the development of higher education is also faster and faster. With economic development and social progress, the demand for talents is also growing. Colleges and universities are the cradle of cultivating excellent talents and undertake the important task of cultivating reserve forces for the country. As an engineer of human soul, English teachers shoulder the responsibility of cultivating excellent talents and the hope of countless parents. For a long time, there have been many problems in the evaluation of College English teachers in China. For example, the assessment objective is too simple, only related to salary promotion and professional title evaluation. In terms of the content of evaluation, they often only pay attention to the quantity and level of scientific research results, ignoring the evaluation of potential, attitude, and other aspects, especially the social responsibility of teachers. Based on the theory of nonlinear random matrix and the theory of competence, this study establishes the quality evaluation model of university teachers in China by combining qualitative and quantitative methods. On this basis, it plays an important role in the selection, training, promotion, and further development of university teachers in China. First of all, according to a large number of domestic and foreign literature on competency, this study summarizes the competency theory and competency model. Using the hypothetical model, a questionnaire was designed and distributed to build a competency model of university teachers from four aspects: professional knowledge, related skills, work attitude, and personality motivation. Finally, the weight of each index in the model is calculated. This study not only constructs the competency evaluation model but also uses the principal component analysis method to calculate it in detail, which provides a good quantitative basis for the selection and performance evaluation of college teachers, so as to prevent the situation beyond its control. It paves an endless bright road for further stable and healthy development. Therefore, the overall research results of this study have greater practical application value and rich connotation.

show abstract

Facial erythema detects diabetic neuropathy using the fusion of machine learning, random matrix theory and self organized criticality

Cited by 3 publications

References 25 publications

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region

Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology

Evaluation and Analysis of English Teaching Ability Based on Nonlinear Random Matrix Model

Contact Info

Product

Resources

About