TIMING OPTIMIZATION OF HEAD AND NECK CT ANGIOGRAPHY VIA THE INVERSE PROBLEM ALGORITHM: <i>in vivo</i> SURVEY FOR 1001 PATIENTS IN 2020–2021

Liang, Chun-Chieh; Pan, Lung-Fa; Chen, Ming‐Hsiang; Deng, Jie; Yang, Deng‐Ho; Lin, Ching Heng; Pan, Lung‐Kwang

doi:10.1142/s0219519421400558

Cited by 3 publications

(14 citation statements)

References 16 publications

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“…In doing so, four out of seven risk factors were preset as average values to imply their general behavior in the CAD patient group because of their minor contributions to the accuracy of the SYNTAX score (cf. Table 3 ) [ 19 , 20 ]. The four minor factors were preset as MAP (0.0), glucose AC (0.68), cTnI (0.90), and CRP (0.74).…”

Section: Discussionmentioning

confidence: 99%

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

et al. 2022

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The quantitative prediction of the SYNTAX score for cardiovascular artery disease patients using the inverse problem algorithm (IPA) technique in artificial intelligence was explored in this study. A 29-term semi-empirical formula was defined according to seven risk factors: (1) age, (2) mean arterial pressure, (3) body surface area, (4) pre-prandial blood glucose, (5) low-density-lipoprotein cholesterol, (6) Troponin I, and (7) C-reactive protein. Then, the formula was computed via the STATISTICA 7.0 program to obtain a compromised solution for a 405-patient dataset with a specific loss function [actual-predicted]2 as low as 3.177, whereas 0.0 implies a 100% match between the prediction and observation via “the lower, the better” principle. The IPA technique first created a data matrix [405 × 29] from the included patients’ data and then attempted to derive a compromised solution of the column matrix of 29-term coefficients [29 × 1]. The correlation coefficient, r2, of the regression line for the actual versus predicted SYNTAX score was 0.8958, showing a high coincidence among the dataset. The follow-up verification based on another 105 patients’ data from the same group also had a high correlation coefficient of r2 = 0.8304. Nevertheless, the verified group’s low derived average AT (agreement) (ATavg = 0.308 ± 0.193) also revealed a slight deviation between the theoretical prediction from the STATISTICA 7.0 program and the grades assigned by clinical cardiologists or interventionists. The predicted SYNTAX scores were compared with earlier reported findings based on a single-factor statistical analysis or scanned images obtained by sonography or cardiac catheterization. Cardiologists can obtain the SYNTAX score from the semi-empirical formula for an instant referral before performing a cardiac examination.

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Section: Discussionmentioning

confidence: 99%

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

et al. 2022

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“…Noteworthy is that only in the study of Pan et al [1], the rank of constant (rank 14/16) was less than any others, namely, ranks 6/16, 7/29, 5/16, 5/22, and 6/22, respectively in the studies [2][3][4][5][6]. Thus, the optimized loss function, Φ (cf.…”

Section: Cross-interactions Among Factorsmentioning

confidence: 94%

“…Nevertheless, the expectation values in the same study could be either the maximal ratio of both left-and-right-arterial-to-upper sinuses (LRA/US) Table 1 Six IPA-related papers were integrated and evaluated in this study. The following acronyms were used: BSA is body surface area, BUN is blood urea nitrogen level, CRP is C-reactive protein, CTA-TT is the suitable triggered timing of computer tomography angiography, CMS is contrast media solution, CMD is contrast media dosage, LDL-C is low-density lipoprotein-cholesterol, LRA/US is the maximal ratio of both left-and-right-arterial-to-upper sinuses, and Pre is the specific pressure of the injector for CMD References [ or suitable triggered timing of computer tomography angiography (CTA-TT) [5,6]. Yet, the preliminary 6)).…”

Section: Integrated Study Of Quantified Prediction Of Expectationmentioning

confidence: 99%

“…Nevertheless, an appropriate trigger timing for CTA preset essentially reduces the exposed dose (CTA trigger timing range from 20 down to 2 sec) for patients who underwent routine examination [6].…”

Section: Integrated Study Of Quantified Prediction Of Expectationmentioning

confidence: 99%

“…The IPA uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. Furthermore, the contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance [ 1 , 2 , 3 , 4 , 5 , 6 ]. Unlike the Taguchi optimization algorithm, which is used to optimize one purpose from a combination of multiple factors [ 7 , 8 , 9 , 10 ], IPA directly estimates the expectation value from a group of data.…”

Section: Introductionmentioning

confidence: 99%

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Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis

Huang

Peng

Lin

et al. 2023

THC

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BACKGROUND: The inverse problem algorithm (IPA) uses mathematical calculations to estimate the expectation value of a specific index according to patient risk factor groups. The contributions of particular risk factors or their cross-interactions can be evaluated and ranked by their importance. OBJECTIVE: This paper quantified the potential risks from multiple biological factors by integrated case studies in clinical diagnosis via the IPA technique. Acting as artificial intelligence field component, this technique constructs a quantified expectation value from multiple patients’ biological index series, e.g., the optimal trigger timing for CTA, a particular drug in blood concentration data, the risk for patients with clinical syndromes. METHODS: Common biological indices such as age, body surface area, mean artery pressure, and others are treated as risk factors upon their normalization to the range from -1.0 to +1.0, with a non-dimensional zero point 0.0 corresponding to the average risk factor index. The patients’ quantified indices are re-arranged into a large data matrix. Next, the inverse and column matrices of the compromised numerical solution are constructed. RESULTS: This paper discusses quasi-Newton and Rosenbrock analyses performed via the STATISTICA program to solve the above inverse problem, yielding the specific expectation value in the form of a multiple-term nonlinear semi-empirical equation. The extensive background, including six previous publications of these authors’ team on IPA, was comprehensively re-addressed and scrutinized, focusing on limitations, stumbling blocks, and validity range of the IPA approach as applied to various tasks of preventive medicine. Other key contributions of this study are detailed estimations of the effect of risk factors’ coupling/cross-interactions on the IPA computations and the convergence rate of the derived semi-empirical equation viz. the final constant term. CONCLUSION: The main findings and practical recommendations are considered useful for preventive medicine tasks concerning potential risks of patients with various clinical syndromes.

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Inverse Problem Algorithm-Based Time-Resolved Imaging of Head and Neck Computed Tomography Angiography Contrast Kinetics with Clinical Testification

Lin,

Peng,

et al. 2023

Diagnostics

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This study mitigated the challenge of head and neck CT angiography by IPA-based time-resolved imaging of contrast kinetics. To this end, 627 cerebral hemorrhage patients with dizziness, brain aneurysm, stroke, or hemorrhagic stroke diagnosis were randomly categorized into three groups, namely, the original dataset (450), verification group (112), and in vivo testified group (65), in the Affiliated BenQ Hospital of Nanjing Medical University. In the first stage, seven risk factors were assigned: age, CTA tube voltage, body surface area, heart rate per minute, cardiac output blood per minute, the actual injected amount of contrast media, and CTA delayed trigger timing. The expectation value of the semi-empirical formula was the CTA number of the patient’s left artery (LA). Accordingly, 29 items of the first-order nonlinear equation were calculated via the inverse problem analysis (IPA) technique run in the STATISTICA 7.0 program, yielding a loss function and variance of 3.1837 and 0.8892, respectively. A dimensionless AT was proposed to imply the coincidence, with a lower AT indicating a smaller deviation between theoretical and practical values. The derived formula was confirmed for the verification group of 112 patients, reaching high coincidence, with average ATavg and standard deviation values of 3.57% and 3.06%, respectively. In the second stage, the formula was refined to find the optimal amount of contrast media for the CTA number of LA approaching 400. Finally, the above procedure was applied to head and neck CTA images of the third group of 65 patients, reaching an average CTA number of LA of 407.8 ± 16.2 and finding no significant fluctuations.

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TIMING OPTIMIZATION OF HEAD AND NECK CT ANGIOGRAPHY VIA THE INVERSE PROBLEM ALGORITHM: in vivo SURVEY FOR 1001 PATIENTS IN 2020–2021

Cited by 3 publications

References 16 publications

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

Quantitative Prediction of SYNTAX Score for Cardiovascular Artery Disease Patients via the Inverse Problem Algorithm Technique as Artificial Intelligence Assessment in Diagnostics

Potential risk quantification from multiple biological factors via the inverse problem algorithm as an artificial intelligence tool in clinical diagnosis

Inverse Problem Algorithm-Based Time-Resolved Imaging of Head and Neck Computed Tomography Angiography Contrast Kinetics with Clinical Testification

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