Reproducibility of an artificial intelligence optical coherence tomography software for tissue characterization: Implications for the design of longitudinal studies.

BACKGROUND: To assess the reproducibility of coronary tissue characterization by an Artificial Intelligence Optical Coherence Tomography software (OctPlus, Shanghai Pulse Medical Imaging Technology Inc.). METHODS: 74 patients presenting with multivessel ST-segment elevation myocardial infarction (STEMI) underwent optical coherence tomography (OCT) of the infarct-related artery at the end of primary percutaneous coronary intervention (PPCI) and during staged PCI (SPCI) within 7 days thereafter in the MATRIX (Minimizing Adverse Hemorrhagic Events by Transradial Access Site and angioX) Treatment-Duration study (ClinicalTrials.gov, NCT01433627). OCT films were run through the OctPlus software. The same region of interest between either side of the stent and the first branch was identified on OCT films for each patient at PPCI and SPCI, thus generating 94 pairs of segments. 42 pairs of segments were re-analyzed for intra-software difference. Five plaque characteristics including cholesterol crystal, fibrous tissue, calcium, lipid, and macrophage content were analyzed for various parameters (span angle, thickness, and area). RESULTS: There was no statistically significant inter-catheter (between PPCI and SPCI) or intra-software difference in the mean values of all the parameters. Inter-catheter correlation for area was best seen for calcification [intraclass correlation coefficient (ICC) 0.86], followed by fibrous tissue (ICC 0.87), lipid (ICC 0.62), and macrophage (ICC 0.43). Some of the inter-catheter relative differences for area measurements were large: calcification 9.75 %; cholesterol crystal 74.10 %; fibrous tissue 5.90 %; lipid 4.66 %; and macrophage 1.23 %. By the intra-software measurements, there was an excellent correlation (ICC > 0.9) for all tissue types. The relative differences for area measurements were: calcification 0.64 %; cholesterol crystal 5.34 %; fibrous tissue 0.19 %; lipid 1.07 %; and macrophage 0.60 %. Features of vulnerable plaque, minimum fibrous cap thickness and lipid area showed acceptable reproducibility. CONCLUSION: The present study demonstrates an overall good reproducibility of tissue characterization by the Artificial Intelligence Optical Coherence Tomography software. In future longitudinal studies, investigators may use discretion in selecting the imaging endpoints and sample size, accounting for the observed relative differences in this study.

The Use of Telemedicine to Improve Hypertension in an Urban Primary Care Clinic and Predictors of Improved Blood Pressure.

BACKGROUND AND OBJECTIVE: Meta-analysis of randomized controlled trials have demonstrated the efficacy of telemedicine in blood pressure (BP) management when compared to conventional care. We initiated a hypertension telehealth clinic in our urban primary care clinic and through this study aim to evaluate the strengths and limitations of telemedicine in hypertension (HTN) control. The primary outcome of the study is to identify the proportion of patients with improved HTN. Secondary outcomes included identifying: predictors for lower BP, predictors of missing telehealth appointments, and comorbid conditions that are more likely to necessitate use of more than 1 antihypertensive medication. METHODS AND ANALYSIS: Patients seen in the HTN telehealth clinic from May 1st, 2022 to October 31st, 2022 were identified. A retrospective chart review was done to compare the BP during in-person visit prior to first telehealth visit, telehealth visit home BP readings and last recorded in-office BP on chart at end of study period. Descriptive statistical analysis, Chi Square test, and multivariable logistic regression was used to analyze data. RESULTS: Of the 234 appointments, 83% were conducted and 154 patients were seen. A remarkable decrease in percentage of patients with BP >140/90 was seen when comparing in-office visit BP to first telehealth visit home BP, 72% versus 45% respectively. No remarkable difference was noted in percentage of patients with BP >140/90 when comparing first telehealth visit home BP to last in-office BP recorded on chart, 45% and 41% respectively. Patients with diabetes had lower odds of missing appointments, adjusted odds ratio (aOR): 0.34 ([0.12-0.91], P = .03). Patients with partners were more likely to have lower BP at the telehealth visit, aOR:3.2 ([1.15-9.86], P = .03) while patients with obstructive sleep apnea (OSA) (aOR 0.27 ([0.08-0.77], P = .02) and CAD, aOR 0.24 ([0.06-0.8], P = .03) were less likely to have lower BP. CONCLUSION: The study demonstrated telemedicine as a great tool to prevent overtreatment of hypertension as significant difference between in-office BP and home BP during telehealth visits was noted. We did not see a significant change in blood pressure when comparing home BP at first telehealth visit to the last in-person clinic BP at end of study period.

Precision Medicine: An Emerging Paradigm for Improved Diagnosis and Safe Therapy in Pediatric Oncology.

Cancer is a lethal disease that kills a great number of people each year. Standard treatments such as chemotherapy or radiation are only effective in a small percentage of individuals due to illness variability. Tumors can be caused by a variety of genetic factors and express a variety of proteins depending on the individual. Because of developments in high-throughput technology, there has been a flood of large-scale biological data produced in recent decades. As a result, the focus of medical research has evolved. It was a once-in-a-lifetime chance for translational research to explore molecular alterations across the entire genome. In this setting, precision medicine was developed, and the possibility of better diagnostic and treatment tools became a reality. This is especially true in the case of cancer, which is becoming more prevalent around the world. The goal of this study is to look at precision medicine technology and its applications to cancer, with a focus on children. The inherent diversity of cancer lends itself to the rapidly expanding field of precision and personalized medicine.

Conjugate unscented transformation-based uncertainty analysis of energy harvesters.

This article presents a probabilistic approach to investigate the effect of parametric uncertainties on the mean power, tip deflection, and tip velocity of linear and nonlinear energy harvesting systems. Recently developed conjugate unscented transformation algorithm is used to compute the statistical moments of the output variables with multidimensional Gaussian uncertainty in parameters. The principle of maximum entropy is used to construct the probability density function of output variables from the knowledge of obtained statistical moments. The probability density functions for mean power were significantly complicated in shape with two and three distinct peaks for the nonlinear monostable and nonlinear bistable harvesters, respectively. Monte-Carlo simulations with N = 8 × 104 samples for monostable harvester and N = 6.5 × 104 samples for bistable harvester were used for validating the probability density functions. It is concluded that conjugate unscented transformation methodology affords a significant computational advantage without compromising accuracy. In addition, using conjugate unscented transformation method, we show that the dependence of mean power on parameters (excitation frequency, excitation amplitude, etc.), when multidimensional uncertainties are present, is decidedly different relative to a purely deterministic trend. The discrepancy in predicted power between the deterministic and uncertain trends for the monostable harvester, for instance, reach a maximum of 100%, 234%, and 110% for base frequency, base acceleration, and magnet gap, respectively. The deterministic trend consistently overestimates the harvested power relative to the uncertain trends. This work, therefore, may have applications in evaluating "worst case scenario" for harvested power. The major advantage of the presented methodology relative to extant techniques in energy harvesting literature is the accurate and computationally effective applicability to multidimensional uncertainty in parameters.

Evaluation of advanced Lukas-Kanade optical flow on thoracic 4D-CT.

Extensive use of high frequency imaging in medical applications permit the estimation of velocity fields which corresponds to motion of landmarks in the imaging field. The focus of this work is on the development of a robust local optical flow algorithm for velocity field estimation in medical applications. Local polynomial fits to the medical image intensity-maps are used to generate convolution operators to estimate the spatial gradients. A novel polynomial window function with a compact support is used to differentially weight the optical flow gradient constraints in the region of interest. Tikhonov regularization is exploited to synthesize a well posed optimization problem and to penalize large displacements. The proposed algorithm is tested and validated on benchmark datasets for deformable image registration. The ten datasets include large and small deformations, and illustrate that the proposed algorithm outperforms or is competitive with other algorithms tested on this dataset, when using mean and variance of the displacement error as performance metrics.

Direction-dependent learning approach for radial basis function networks.

Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.