Jordan Chamberlin

Bio: Jordan Chamberlin is an academic researcher from Medical University of South Carolina. The author has contributed to research in topics: Medicine & Radiology. The author has an hindex of 2, co-authored 5 publications receiving 10 citations.

Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: accuracy and prognostic value

Abstract: Artificial intelligence (AI) in diagnostic radiology is undergoing rapid development. Its potential utility to improve diagnostic performance for cardiopulmonary events is widely recognized, but the accuracy and precision have yet to be demonstrated in the context of current screening modalities. Here, we present findings on the performance of an AI convolutional neural network (CNN) prototype (AI-RAD Companion, Siemens Healthineers) that automatically detects pulmonary nodules and quantifies coronary artery calcium volume (CACV) on low-dose chest CT (LDCT), and compare results to expert radiologists. We also correlate AI findings with adverse cardiopulmonary outcomes in a retrospective cohort of 117 patients who underwent LDCT. A total of 117 patients were enrolled in this study. Two CNNs were used to identify lung nodules and CACV on LDCT scans. All subjects were used for lung nodule analysis, and 96 subjects met the criteria for coronary artery calcium volume analysis. Interobserver concordance was measured using ICC and Cohen’s kappa. Multivariate logistic regression and partial least squares regression were used for outcomes analysis. Agreement of the AI findings with experts was excellent (CACV ICC = 0.904, lung nodules Cohen’s kappa = 0.846) with high sensitivity and specificity (CACV: sensitivity = .929, specificity = .960; lung nodules: sensitivity = 1, specificity = 0.708). The AI findings improved the prediction of major cardiopulmonary outcomes at 1-year follow-up including major adverse cardiac events and lung cancer (AUCMACE = 0.911, AUCLung Cancer = 0.942). We conclude the AI prototype rapidly and accurately identifies significant risk factors for cardiopulmonary disease on standard screening low-dose chest CT. This information can be used to improve diagnostic ability, facilitate intervention, improve morbidity and mortality, and decrease healthcare costs. There is also potential application in countries with limited numbers of cardiothoracic radiologists.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Abstract: Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S.A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.Overall ICC was 0.820 (95% CI 0.790-0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861-0.920) for the neural network and 0.936 (95% CI 0.918-0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics.

Abstract: Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging

Abstract: Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

Radiation-Induced Lung Injury-Current Perspectives and Management.

Abstract: Radiotherapy plays an important role in the treatment of localized primary malignancies involving the chest wall or intrathoracic malignancies. Secondary effects of radiotherapy on the lung result in radiation-induced lung disease. The phases of lung injury from radiation range from acute pneumonitis to chronic pulmonary fibrosis. Radiation pneumonitis is a clinical diagnosis based on the history of radiation, imaging findings, and the presence of classic symptoms after exclusion of infection, pulmonary embolism, heart failure, drug-induced pneumonitis, and progression of the primary tumor. Computed tomography (CT) is the preferred imaging modality as it provides a better picture of parenchymal changes. Lung biopsy is rarely required for the diagnosis. Treatment is necessary only for symptomatic patients. Mild symptoms can be treated with inhaled steroids while subacute to moderate symptoms with impaired lung function require oral corticosteroids. Patients who do not tolerate or are refractory to steroids can be considered for treatment with immunosuppressive agents such as azathioprine and cyclosporine. Improvements in radiation technique, as well as early diagnosis and appropriate treatment with high-dose steroids, will lead to lower rates of pneumonitis and an overall good prognosis.

The Future of Lung Cancer Screening: Current Challenges and Research Priorities

Abstract: Lung cancer is the leading cause of cancer-related deaths worldwide, primarily because most people present when the stage is too advanced to offer any reasonable chance of cure. Over the last two decades, evidence has accumulated to show that early detection of lung cancer, using low-radiation dose computed tomography, in people at higher risk of the condition reduces their mortality. Many countries are now making progress with implementing programmes, although some have concerns about cost-effectiveness. Lung cancer screening is complex, and many factors influence clinical and cost-effectiveness. It is important to develop strategies to optimise each element of the intervention from selection and participation through optimal scanning, management of findings and treatment. The overall aim is to maximise benefits and minimise harms. Additional integrated interventions must include at least smoking cessation. In this review, we summarize the evidence that has accumulated to guide optimisation of lung cancer screening, discuss the remaining open questions about the best approach and identify potential barriers to successful implementation.