Solving the inverse problem based on UPEMD for electrocardiographic imaging

Zhang, Yadan; Wu, Jian; Li, Yifu; Li, Haiying; Lin, Jun; Hairui, Li

doi:10.1016/j.bspc.2022.103665

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…Sixth, regarding prediction reliability and uncertainty quantification, the current applications of ML in the spinal imaging field (nay, in the entire biomedical informatics field) generally seem to focus on the prediction task, and the model performance is evaluated with such metrics as accuracy, ROC, and sensitivity [ 127 , 128 , 129 , 130 ]. However, the much lower attended reliability and uncertainty of the predictions can be of great significance in medical diagnosis [ 131 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Cui

Zhu

Duan

et al. 2022

IJERPH

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Spinal maladies are among the most common causes of pain and disability worldwide. Imaging represents an important diagnostic procedure in spinal care. Imaging investigations can provide information and insights that are not visible through ordinary visual inspection. Multiscale in vivo interrogation has the potential to improve the assessment and monitoring of pathologies thanks to the convergence of imaging, artificial intelligence (AI), and radiomic techniques. AI is revolutionizing computer vision, autonomous driving, natural language processing, and speech recognition. These revolutionary technologies are already impacting radiology, diagnostics, and other fields, where automated solutions can increase precision and reproducibility. In the first section of this narrative review, we provide a brief explanation of the many approaches currently being developed, with a particular emphasis on those employed in spinal imaging studies. The previously documented uses of AI for challenges involving spinal imaging, including imaging appropriateness and protocoling, image acquisition and reconstruction, image presentation, image interpretation, and quantitative image analysis, are then detailed. Finally, the future applications of AI to imaging of the spine are discussed. AI has the potential to significantly affect every step in spinal imaging. AI can make images of the spine more useful to patients and doctors by improving image quality, imaging efficiency, and diagnostic accuracy.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Cui

Zhu

Duan

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…According to the evaluation criteria of Swets [ 65 ], when the AUC = 0.5, the diagnosis is completely ineffective; when the AUC is less than 0.5, it is not in line with the actual situation; it is generally believed that 0.5 < AUC ≤ 0.7 indicates low diagnostic value. It is only for reference in practical applications; 0.7 < AUC ≤ 0.9 indicates a certain degree of accuracy and can be used in clinical diagnosis, with a medium diagnostic value; AUC > 0.9 indicates a relatively high diagnostic value, which can be used in clinical diagnosis as an important diagnostic basis [ 66 , 67 ]. In the results of this study, the AUC was 0.89, close to 0.9, so we think that saliva cortisol testing can be considered an auxiliary method for the clinical diagnosis of depression, and its diagnostic value is moderate.…”

Section: Discussionmentioning

confidence: 99%

Stimulated Parotid Saliva Is a Better Method for Depression Prediction

et al. 2022

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Background: Saliva cortisol is considered to be a biomarker of depression prediction. However, saliva collection methods can affect the saliva cortisol level. Objective: This study aims to determine the ideal saliva collection method and explore the application value of saliva cortisol in depression prediction. Methods: 30 depressed patients and 30 healthy controls were instructed to collect saliva samples in the morning with six collection methods. Simultaneous venous blood was collected. Enzyme-linked immunosorbent assay was used to determine the cortisol level. The 24-observerrated Hamilton depression rating scale (HAMD-24) was used to assess the severity of depression. Results: The significant differences in saliva cortisol levels depend on the saliva collection methods. The level of unstimulated whole saliva cortisol was most correlated with blood (r = 0.91). The stimulated parotid saliva cortisol can better predict depression. The area under the curve was 0.89. In addition, the saliva cortisol level of the depression patients was significantly higher than the healthy controls. The correlation between the cortisol level and the HAMD-24 score was highly significant. The higher the saliva cortisol level, the higher the HAMD-24 score. Conclusions: All the above findings point to an exciting opportunity for non-invasive monitoring of cortisol through saliva.

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“…Polyuria, polyphagia, polydipsia, abrupt weight loss, hazy eyesight, etc., are typical DM symptoms [ 62 , 63 ]. Recent research by Harris et al [ 64 ] indicates that DM may be present 7 or even 12 years prior to clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Obtaining a Reliable Diagnostic Biomarker for Diabetes Mellitus by Standardizing Salivary Glucose Measurements

et al. 2022

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Salivary glucose is frequently utilized in diabetes mellitus (DM), and it might be proposed as a potential biomarker candidate for DM, as it is non-invasive and cost-effective and achieves adequate diagnostic performance for DM patients. However, salivary glucose levels may change under specific conditions. It is consequently essential to maintain a consistent strategy for measuring saliva, taking into account the possibility of external factors influencing salivary glucose levels. In this study, we analyzed salivary glucose levels under different handling conditions and donor-dependent factors, including age, interdiurnal variations, and collection and processing methods. A structured questionnaire was used to determine the symptoms and predisposing factors of DM. The glucose oxidase peroxidase method was used to estimate glucose levels in the blood and saliva of people in a fasting state. The aim of this study is to investigate the effect of such conditions on salivary glucose levels. We found that these extraneous variables should be taken into account in the future when salivary glucose is used as a predictive biomarker for DM.

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Solving the inverse problem based on UPEMD for electrocardiographic imaging

Cited by 6 publications

References 37 publications

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Artificial Intelligence in Spinal Imaging: Current Status and Future Directions

Stimulated Parotid Saliva Is a Better Method for Depression Prediction

Obtaining a Reliable Diagnostic Biomarker for Diabetes Mellitus by Standardizing Salivary Glucose Measurements

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