Browsing by Author "Fricks, Rafael"
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Item Open Access A comparison of COVID-19 and imaging radiation risk in clinical patient populations.(J Radiol Prot, 2020-10-07) Ria, Francesco; Fu, Wanyi; Chalian, Hamid; Abadi, Ehsan; Segars, Paul W; Fricks, Rafael; Khoshpouri, Pegah; Samei, EhsanOBJECTIVE: The outbreak of coronavirus SARS-COV2 affected more than 180 countries necessitating fast and accurate diagnostic tools. Reverse transcriptase polymerase chain reaction (RT-PCR) has been identified as a gold standard test with Chest CT and Chest Radiography showing promising results as well. However, radiological solutions have not been used extensively for the diagnosis of COVID-19 disease, partly due to radiation risk. This study aimed to provide quantitative comparison of imaging radiation risk versus COVID risk. METHODS: The analysis was performed in terms of mortality rate per age group. COVID-19 mortality was extracted from epidemiological data across 299,004 patients published by ISS-Integrated surveillance of COVID-19 in Italy. For radiological risk, the study considered 659 Chest CT performed in adult patients. Organ doses were estimated using a Monte Carlo method and then used to calculate Risk Index that was converted into an upper bound for related mortality rate following NCI-SEER data. RESULTS: COVID-19 mortality showed a rapid rise for ages >30 years old (min:0.30%; max:30.20%), whereas only 1 death was reported in the analyzed patient cohort for ages <20 years old. The rates decreased for radiation risk across age groups. The median mortality rate across all ages for Chest-CT and Chest-Radiography were 0.007% (min:0.005%; max:0.011%) and 0.0003% (min:0.0002%; max:0.0004%), respectively. CONCLUSIONS: COVID-19, Chest Radiography, and Chest CT mortality rates showed different magnitudes and trends across age groups. In higher ages, the risk of COVID-19 far outweighs that of radiological exams. Based on risk comparison alone, Chest Radiography and CT for COVID-19 care is justified for patients older than 20 and 30 years old, respectively. Notwithstanding other aspects of diagnosis, the present results capture a component of risk consideration associated with the use of imaging for COVID. Once integrated with other diagnostic factors, they may help inform better management of the pandemic.Item Open Access Classification of COVID-19 in chest radiographs: assessing the impact of imaging parameters using clinical and simulated images(Medical Imaging 2021: Computer-Aided Diagnosis, 2021-02-15) Fricks, Rafael; Abadi, Ehsan; Ria, Francesco; Samei, EhsanAs computer-aided diagnostics develop to address new challenges in medical imaging, including emerging diseases such as COVID-19, the initial development is hampered by availability of imaging data. Deep learning algorithms are particularly notorious for performance that tends to improve proportionally to the amount of available data. Simulated images, as available through advanced virtual trials, may present an alternative in data-constrained applications. We begin with our previously trained COVID-19 x-ray classification model (denoted as CVX) that leveraged additional training with existing pre-pandemic chest radiographs to improve classification performance in a set of COVID-19 chest radiographs. The CVX model achieves demonstrably better performance on clinical images compared to an equivalent model that applies standard transfer learning from ImageNet weights. The higher performing CVX model is then shown to generalize effectively to a set of simulated COVID-19 images, both quantitative comparisons of AUCs from clinical to simulated image sets, but also in a qualitative sense where saliency map patterns are consistent when compared between sets. We then stratify the classification results in simulated images to examine dependencies in imaging parameters when patient features are constant. Simulated images show promise in optimizing imaging parameters for accurate classification in data-constrained applications.Item Open Access End-to-End Outpatient Clinic Modeling for Performance Optimization and Scheduling in Health Care Service(2018) Fricks, RafaelDecisions in health care must often be made under inherent uncertainty; from treating patients, to provisioning medical devices, to operational decisions at an outpatient clinic. The outcomes depend on the health of patients as well as the availability of health care professionals and resources. Complex models of clinic performance allow for experiments with new schedules and resource levels without the time, cost, unfeasibility, or risk of testing new policies in real clinics. Model-based methods quantify the effect of various uncertain factors such as the availability of personnel on health care quality indicators like patient wait times in a clinic.
Despite their purported value, few opportunities have existed to test models from data collection through optimization. This dissertation develops a clinic model from end-to-end, beginning with a description of the medical practice, to data collection, to model validation, to optimization. Specialty medical practice is abstracted into treatment steps, measured electronically, and verified through systematic observation. These data are anonymized and made available for researchers. A validation framework uses the data to develop and test candidate models, selecting one that maximizes predictive accuracy while retaining interpretability and reproducibility. The resulting model is used in improving schedules via heuristic optimization. Clustering the results reveals clinic performance groups that represent different goals in clinic quality.
Item Open Access Justification of radiological procedures in COVID-19 pandemic based on radiation risk only(2020-12-02) Ria, Francesco; Fu, wanyi; Chalian, Hamid; Segars, W; Fricks, Rafael; Khoshpouri, Pegah; Samei, EhsanPurpose. Radiologic procedures are recommended based on benefit-to-risk justification. In X-ray imaging, while the benefit is often immediate for the patient, the associated radiation burden risk is a longer-term effect. Such a temporal gap can bias the justification process in imaging utilization, particularly during a spreading pandemic like COVID-19 in which fast and accurate diagnostic tools are highly needed. Chest CT and chest radiography (CXR) have shown promising results in the diagnosis and management of COVID-19, providing support to the standard RT-PCR test. However, several institutions are discouraging the use of imaging for this purpose, partly due to radiation risk. This study aims to provide quantitative data towards an effective risk-to benefit analysis for the justification of radiological studies in the diagnosis and management of COVID-19 to guide clinicians and decision making. Materials and Methods. The analysis was performed in terms of mortality rate per age group. COVID-19 mortality was extracted from epidemiological data across 159,107 patients in Italy. For radiological risk, the study considered 659 Chest CT scans performed in adult patients. Organ doses were estimated using a Monte Carlo based method and then used to calculate a risk index that was converted into a related 5-year mortality rate (SEER, NCI). Results. COVID-19 mortality showed a rapid rise for ages >30 years old (min: 0.30%; max: 30.20%). Only 1 death was reported in the analyzed patient cohort for ages <20 years old. The mortality rates based on radiation exposure decreased across age groups. The median mortality rate across all ages for Chest CT and CXR were 0.72% (min: 0.46%; max: 1.10%) and 0.03% (min: 0.02%; max: 0.04%), respectively. Conclusions. Radiation risk is not the only factor that should be taken into account for justifying the use of imaging in COVID care; nonetheless, it is an essential factor of consideration. The risk associated with COVID-19, CT, and CXR exhibited different magnitudes and trends across age groups. In higher ages, the risk of COVID-19 far outweighed that of radiological exams. Based on risk comparison alone, CXR and Chest CT are justified for COVID-19 care of patients older than 30 and 50 years old, respectively. Clinical Relevance statement (max 200 characters, with spaces) Towards a comprehensive radiological procedures risk-to-benefit assessment, CT and CXR should not be a priori excluded in the diagnosis and management of the COVID-19.