Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Background: The prevalence and outcome of coronavirus disease 2019 (COVID-19) in rural areas is unknown. Methods: This is a multi-center retrospective cohort study of hospitalized patients diagnosed with COVID-19 from April 5, 2020 to December 31, 2020. The data were extracted from 13 facilities in the Appalachian Regional Healthcare system that share the same electronic health record using ICD-10-CM codes. Results: The number of patients diagnosed with COVID-19 per facility ranged from 5 to 535 with a median of 106 patients. Total mortality was 11.4% and ranged from 0% to 22.6% by facility (median: 9.0%). Non-survivors had a greater prevalence of congestive heart failure (CHF), hypertension, type 2 diabetes mellitus, stroke, transient ischemic attack (TIA), and pulmonary embolism. Patients who died were also more likely to have had chronic obstructive pulmonary disease (COPD), acute respiratory failure (ARF), liver cirrhosis, chronic kidney disease (CKD), dementia, cancer, anemia, and opiate dependence. Conclusion: The aging population, multiple co-morbidities, and health-related behaviors make rural patients vulnerable to COVID-19. A better understanding of the disease in rural areas is crucial, given its heightened vulnerability to adverse outcomes.

Introduction Acute pancreatitis (AP) and acute aortic dissection (AAD) are medical emergencies that must be promptly recognized to avoid the development of life-threatening complications. Both of these diseases can present with chest or epigastric pain which can radiate to the back, thus, early suspicion based on clinical presentation and risk factors is essential. We present the case of a 56-year-old patient initially diagnosed with AP who was later found to have an AAD. Clinical Findings A 56-year-old man with a history of alcohol abuse presented with 1 day of diffuse abdominal pain, nausea and vomiting. His lipase was 3,909 U/L and creatinine was 2.19 mg/dL and he was diagnosed with acute alcoholic pancreatitis with acute kidney injury. A non-contrast computed tomography (NCCT) scan of the abdomen showed aortic calcifications. He received 3.8 liters of fluids after which he developed acute respiratory distress requiring intubation. A workup for extracorporeal membrane oxygenation (ECMO) was initiated, given the suspicion of acute respiratory distress syndrome (ARDS) due to pancreatitis. This revealed an AAD with severe aortic regurgitation on transthoracic echocardiography (TTE). CT angiogram showed type A AAD involving the aortic root, ascending aorta, descending aorta, suprarenal and infrarenal abdominal aorta. The celiac axis, superior mesenteric artery (SMA) and inferior mesenteric artery (IMA) were patent. Outcomes The patient underwent type A AAD repair with mechanical aortic valve replacement and survived the acute event. His AP resolved and he was discharged home with appropriate follow up. Conclusion We hypothesize that if our patient was not assessed for ECMO, the finding of AAD would have been a diagnostic challenge. AP secondary to AAD is rare but a high index of suspicion is required for diagnosis.

The effect of COVID-19 vaccination in orthotopic heart transplant (OHT) patients is unknown. After OHT, patients are increased risk of COVID infection and hospitalization. We retrospectively analyzed 119 patients who underwent OHT between 2017 and 2021. Eleven patients were excluded who died prior to the COVID outbreak in the United States. The mean age was 51 years (IQR 26). The known vaccination rate (partial or complete) was 83%. The overall infection rate was 14% (17 COVID cases were identified.) Five patients were infected prior to the availability of the COVID vaccine. Of the remaining 2 (16%) and 5 (42%) were in vaccinated and unvaccinated patients respectively. The hospitalization rate due to COVID infection or COVID-related complications such as supplemental oxygen use was 29%. All hospitalized subjects underwent changes in their antirejection therapies, and half required oxygen supplementation therapy at discharge. No COVID-related deaths were identified. There were 2 partially/fully vaccinated patients at the time of COVID infection. One patient had mild symptoms and did not require hospitalization while the other patient was asymptomatic. Hospitalization rates were markedly higher in the OHT cohort compared to Kentucky state data (29% vs 4%.) Multiple factors contribute to this finding. Patients with OHT have more co-morbidities and after OHT and immunosuppressant therapy blunts host response to infection placing these patients at higher risk of complications. There was a higher vaccination rate in our OHT cohort compared to Kentucky state data (83% vs 61%). Breakthrough COVID infection was found in only 4% of OHT patients strongly supporting the efficacy of the vaccination in this immunosuppressant subgroup. While there were no COVID related deaths in our cohort, downstream complications related to immunosuppression changes and organ rejection detection require long term follow up. The vaccine has proved highly efficacious in this group and should be implemented up front, prior to transplantation. We suggest pre-transplant COVID-19 vaccination should become mandatory in patients being evaluated for OHT.