Impact of easing COVID-19 safety measures on trauma computed tomography imaging volumes.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to substantial disruptions in healthcare staffing and operations. Stay-at-home (SAH) orders and limitations in social gathering implemented in spring 2020 were followed by initial decreases in healthcare and imaging utilization. This study aims to evaluate the impact of subsequent easing of SAH on trauma volumes, demand for, and turnaround times for trauma computed tomography (CT) exams, hypothesizing that after initial decreases, trauma volumes have increased as COVID safety measures have been reduced. METHODS: Patient characteristics, CT imaging volumes, and turnaround time were analyzed for all adult activated emergency department trauma patients requiring CT imaging at a single Level-I trauma center (1/2018-2/2022) located in the sixth most populous county in the USA. Based on COVID safety measures in place in the state of California, three time periods were compared: baseline (PRE, 1/1/2018-3/19/2020), COVID safety measures (COVID, 3/20/2020-1/25/2021), and POST (1/26/2021-2/28/2022). RESULTS: There were 16,984 trauma patients across the study (PRE = 8289, COVID = 3139, POST = 5556). The average daily trauma patient volumes increased significantly in the POST period compared to the PRE and COVID periods (13.9 vs. 10.3 vs. 10.1, p < 0.001), with increases in both blunt (p < 0.001) and penetrating (p = 0.002) trauma. The average daily number of trauma CT examinations performed increased significantly in the POST period compared to the PRE and COVID periods (56.7 vs. 48.3 vs. 47.6, p < 0.001), with significant increases in average turnaround time (47 min vs. 31 and 37, p < 0.001). CONCLUSION: After initial decreases in trauma radiology volumes following stay-at-home orders, subsequent easing of safety measures has coincided with increases in trauma imaging volumes above pre-pandemic levels and longer exam turnaround times.

Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study.

Background Point-of-care (POC) MRI is a bedside imaging technology with fewer than five units in clinical use in the United States and a paucity of scientific studies on clinical applications. Purpose To evaluate the clinical and operational impacts of deploying POC MRI in emergency department (ED) and intensive care unit (ICU) patient settings for bedside neuroimaging, including the turnaround time. Materials and Methods In this preliminary retrospective study, all patients in the ED and ICU at a single academic medical center who underwent noncontrast brain MRI from January 2021 to June 2021 were investigated to determine the number of patients who underwent bedside POC MRI. Turnaround time, examination limitations, relevant findings, and potential CT and fixed MRI findings were recorded for patients who underwent POC MRI. Descriptive statistics were used to describe clinical variables. The Mann-Whitney U test was used to compare the turnaround time between POC MRI and fixed MRI examinations. Results Of 638 noncontrast brain MRI examinations, 36 POC MRI examinations were performed in 35 patients (median age, 66 years [IQR, 57-77 years]; 21 women), with one patient undergoing two POC MRI examinations. Of the 36 POC MRI examinations, 13 (36%) occurred in the ED and 23 (64%) in the ICU. There were 12 of 36 (33%) POC MRI examinations interpreted as negative, 14 of 36 (39%) with clinically significant imaging findings, and 10 of 36 (28%) deemed nondiagnostic for reasons such as patient motion. Of 23 diagnostic POC MRI examinations with comparison CT available, three (13%) demonstrated acute infarctions not apparent on CT scans. Of seven diagnostic POC MRI examinations with subsequent fixed MRI examinations, two (29%) demonstrated missed versus interval subcentimeter infarctions, while the remaining demonstrated no change. The median turnaround time of POC MRI was 3.4 hours in the ED and 5.3 hours in the ICU. Conclusion Point-of-care (POC) MRI was performed rapidly in the emergency department and intensive care unit. A few POC MRI examinations demonstrated acute infarctions not apparent at standard-of-care CT examinations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Anzai and Moy in this issue.

RNA-Seq analysis reveals gene expression changes induced by IL-6 trans-signaling activation in retinal endothelial cells.

BACKGROUND: Increasing evidence suggests that interleukin-6 (IL-6) trans-signaling plays a critical role in the pathogenesis of diabetic retinopathy (DR). We have previously shown that activation of IL-6 trans-signaling induces barrier dysfunction in human retinal endothelial cells (HRECs). However, the molecular mechanisms underlying these effects are not clear. The purpose of this study was to discover global gene expression changes in HRECs following activation of IL-6 trans-signaling. METHODS: HRECs were treated with IL-6 and soluble IL-6R to activate IL-6 trans-signaling, and sgp130Fc treatment was used for IL-6 trans-signaling inhibition. RNA-Seq analyses were performed for global gene expression profiling. Differential expression was determined using DESeq2, and bioinformatic analyses were performed to associate the differentially expressed genes with biological functions and pathways. RESULTS: Our analyses revealed 445 differentially expressed genes (318 upregulated and 127 downregulated) in HRECs after IL-6 trans-signaling activation. We identified several novel genes not previously associated with IL-6 signaling or endothelial dysfunction. Leukocyte adhesion, diapedesis and chemokine signaling pathways are highly enriched in differentially expressed genes. Inhibition of IL-6 trans-signaling with sgp130Fc abrogated these changes, thus highlighting the therapeutic potential of this drug. CONCLUSIONS: This study identified significant gene expression changes caused by IL-6 trans-signaling activation in HRECs. Identification of such changes has the potential to uncover the precise molecular mechanisms of IL-6 trans-signaling mediated effects in the pathology of DR.

A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI. MATERIALS AND METHODS: This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation. RESULTS: The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974. CONCLUSION: A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., "flattening the curve") have included shelter-in-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services. METHODS: We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes. RESULTS: ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001). CONCLUSION: Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases.

Characteristics and Outcomes of Pediatric Patients With Ebola Virus Disease Admitted to Treatment Units in Liberia and Sierra Leone: A Retrospective Cohort Study.

BACKGROUND: The clinical and virologic characteristics of Ebola virus disease (EVD) in children have not been thoroughly documented. METHODS: Consecutive children aged <18 years with real-time polymerase chain reaction (RT-PCR)-confirmed EVD were enrolled retrospectively in 5 Ebola treatment units in Liberia and Sierra Leone in 2014/2015. Data collection and medical management were based on standardized International Medical Corps protocols. We performed descriptive statistics, multivariate logistic regression, and Kaplan-Meier survival analyses. RESULTS: Of 122 children enrolled, the median age was 7 years and one-third were aged <5 years. The female-to-male ratio was 1.3. The most common clinical features at triage and during hospitalization were fever, weakness, anorexia, and diarrhea, although 21% of patients were initially afebrile and 6 patients remained afebrile. Bleeding was rare at presentation (5%) and manifested subsequently in fewer than 50%. The overall case fatality rate was 57%. Factors associated with death in bivariate analyses were age <5 years, bleeding at any time during hospitalization, and high viral load. After adjustment with logistic regression modeling, the odds of death were 14.8-fold higher if patients were aged <5 years, 5-fold higher if the patient had any evidence of bleeding, and 5.2-fold higher if EVD RT-PCR cycle threshold value was ≤20. Plasmodium parasitemia had no impact on EVD outcomes. CONCLUSIONS: Age <5 years, bleeding, and high viral loads were poor prognostic indicators of children with EVD. Research to understand mechanisms of these risk factors and the impact of dehydration and electrolyte imbalance will improve health outcomes.

Midupper Arm Circumference Outperforms Weight-Based Measures of Nutritional Status in Children with Diarrhea.

BACKGROUND: Undernutrition contributes to 45% of all deaths in children <5 y of age worldwide, with a large proportion of those deaths caused by diarrhea. However, no validated tools exist for assessing undernutrition in children with diarrhea and possible dehydration. OBJECTIVE: This study assessed the validity of different measures of undernutrition in children with diarrhea. METHODS: A prospective cohort study was conducted at an urban hospital in Bangladesh. Children <60 mo of age presenting to the hospital rehydration unit with acute diarrhea were eligible for enrollment. Study staff randomly selected 1196 children for screening, of which 1025 were eligible, 850 were enrolled, and 721 had complete data for analysis. Anthropometric measurements, including weight-for-age z score (WAZ), weight-for-length z score (WLZ), midupper arm circumference (MUAC), and midupper arm circumference z score (MUACZ), were calculated pre- and posthydration in all patients. Measurements were evaluated for their ability to correctly identify undernutrition in children with varying degrees of dehydration. RESULTS: Of the 721 patients with full data for analysis, the median percent dehydration was 4%. Of the 4 measures evaluated, MUAC and MUACZ demonstrated 92-94% agreement pre- and posthydration compared with 69-76% for WAZ and WLZ. Although each 1% change in hydration status was found to change weight-for-age by 0.0895 z scores and weight-for-length by 0.1304 z scores, MUAC and MUACZ were not significantly affected by dehydration status. Weight-based measures misclassified 12% of children with severe underweight and 14% with severe acute malnutrition (SAM) compared with only 1-2% for MUAC and MUACZ. CONCLUSIONS: MUAC and MUACZ were the most accurate predictors of undernutrition in children with diarrhea. WAZ and WLZ were significantly affected by dehydration status, leading to the misdiagnosis of many patients on arrival with severe underweight and SAM. This trial was registered at clinicaltrials.gov as NCT02007733.

Derivation and Internal Validation of the Ebola Prediction Score for Risk Stratification of Patients With Suspected Ebola Virus Disease.

STUDY OBJECTIVE: The current outbreak of Ebola virus disease in West Africa is the largest on record and has overwhelmed the capacity of local health systems and the international community to provide sufficient isolation and treatment of all suspected cases. The goal of this study is to develop a clinical prediction model that can help clinicians risk-stratify patients with suspected Ebola virus disease in the context of such an epidemic. METHODS: A retrospective analysis was performed of patient data collected during routine clinical care at the Bong County Ebola Treatment Unit in Liberia during its first 16 weeks of operation. The predictive power of 14 clinical and epidemiologic variables was measured against the primary outcome of laboratory-confirmed Ebola virus disease, using logistic regression to develop a final prediction model. Bootstrap sampling was used to assess the internal validity of the model and estimate its performance in a simulated validation cohort. RESULTS: Ebola virus disease testing results were available for 382 (97%) of 395 patients admitted to the Ebola treatment unit during the study period. A total of 160 patients (42%) tested positive for Ebola virus disease. Logistic regression analysis identified 6 variables independently predictive of laboratory-confirmed Ebola virus disease, including sick contact, diarrhea, loss of appetite, muscle pains, difficulty swallowing, and absence of abdominal pain. The Ebola Prediction Score, constructed with these 6 variables, had an area under the receiver operator characteristic curve of 0.75 (95% confidence interval 0.70 to 0.80) for the prediction of laboratory-confirmed Ebola virus disease. Patients with higher Ebola Prediction Scores had higher likelihoods of laboratory-confirmed Ebola virus disease. CONCLUSION: The Ebola Prediction Score can be used by clinicians as an adjunct to current Ebola virus disease case definitions to risk-stratify patients with suspected Ebola virus disease. Clinicians can use this new tool for the purpose of cohorting patients within the suspected-disease ward of an Ebola treatment unit or community-based isolation center to prevent nosocomial infection or as a triage tool when patient numbers overwhelm available capacity. Given the inherent limitations of clinical prediction models, however, a low-cost, point-of-care test that can rapidly and definitively exclude Ebola virus disease in patients should be a research priority.

Candida and cardiovascular implantable electronic devices: a case of lead and native aortic valve endocarditis and literature review.

Use of cardiovascular implantable electronic devices (CIED), including permanent pacemakers (PPM) and implantable cardioverter defibrillators (ICD), has increased dramatically over the past two decades. Most CIED infections are caused by staphylococci. Fungal causes are rare and their prognosis is poor. To our knowledge, there has not been a previously reported case of multifocal Candida endocarditis involving both a native left-sided heart valve and a CIED lead. Here, we report the case of a 70-year-old patient who presented with nausea, vomiting, and generalised fatigue, and was found to have Candida glabrata endocarditis involving both a native aortic valve and right atrial ICD lead. We review the literature and summarise four additional cases of CIED-associated Candida endocarditis published from 2009 to 2014, updating a previously published review of cases prior to 2009. We additionally review treatment guidelines and discuss management of CIED-associated Candida endocarditis.

Invertebrate models of fungal infection.

The morbidity, mortality and economic burden associated with fungal infections, together with the emergence of fungal strains resistant to current antimicrobial agents, necessitate broadening our understanding of fungal pathogenesis and discovering new agents to treat these infections. Using invertebrate hosts, especially the nematode Caenorhabditis elegans and the model insects Drosophila melanogaster and Galleria mellonella, could help achieve these goals. The evolutionary conservation of several aspects of the innate immune response between invertebrates and mammals makes the use of these simple hosts an effective and fast screening method for identifying fungal virulence factors and testing potential antifungal compounds. The purpose of this review is to compare several model hosts that have been used in experimental mycology to-date and to describe their different characteristics and contribution to the study of fungal virulence and the detection of compounds with antifungal properties. This article is part of a Special Issue entitled: Animal Models of Disease.

Work-related musculoskeletal disorders affecting diagnostic radiologists and prophylactic physical therapy regimen.

The shift from film to PACS in reading rooms, coupled with escalating case volumes, exposes radiologists to the issues of the modern computer workstation including computer work posture and work-related musculoskeletal disorders (WMSD). Common WMSDs affecting the neck and upper extremities include cervical myofascial pain, shoulder tendonitis, lateral epicondylitis, carpal tunnel syndrome, and cubital tunnel syndrome. This review examines each pathology along with its pathogenesis, clinical features, physical exam findings, and potential risk factors. Furthermore, a comprehensive 11-part physical therapy regimen that is both prophylactic and therapeutic is illustrated and described in detail. One of the objectives of this review is to advocate for the inclusion of a physical therapy regimen in the working routine of diagnostic radiologists to prevent WMSDs. A brief daily commitment to this regimen can help radiologists remain healthy and productive in order to deliver optimal patient care throughout their careers.

Utilization of Re-VASC, the Novel Retroperitoneal Neovascularity Scoring System, for Characterization of T1a Small Renal Masses.

Purpose: Early characterization of small (T1a, <4 cm) renal masses is imperative for patient care and treatment planning. Renal biopsy is a sensitive and specific procedure that can accurately differentiate small renal masses as malignant or benign. However, it is an invasive procedure with a nonnegligible complication rate and is not performed routinely at most institutions. In this study, we sought to apply the Retroperitoneal Vascularity Assessment and Scoring in Carcinoma (Re-VASC) scoring system to T1a renal masses and analyzed whether it could differentiate these masses as benign or malignant. Methods: We obtained Institutional Review Board approval to retrospectively examine the records of all patients who presented to our single, urban academic referral center for surgical treatment of renal cell carcinoma (RCC). For the malignant group, patients with a diagnosis of T1a RCC from pathologic evaluation were included. Additionally, patients with a histopathological diagnosis of a T1a nonmalignant renal mass (fat poor-angiomyolipoma or oncocytoma) were included in our benign group. Results: This study includes 57 benign and 69 malignant T1a renal tumors. Average size for benign and malignant masses were 2.47 and 2.63, respectively (p = 0.267). Analysis demonstrated no significant difference between both groups in terms of sex, laterality, or size. The average Re-VASC score of benign and malignant masses was 0.175 and malignant masses was 0.784, respectively (p < 0.001). Additionally, the Re-VASC score was independently associated with malignancy with an odds ratio of 2.223 (p = 0.0109). Conclusion: The Re-VASC scoring system exhibits significantly greater values for malignant T1a renal masses when compared to benign masses. As a result, it shows promise as an adjunctive tool to renal biopsy for clinical decision-making. Further assessment of Re-VASC's true efficacy as a diagnostic marker will include prospective evaluation of a larger multicenter population.

Generating colloquial radiology reports with large language models.

OBJECTIVES: Patients are increasingly being given direct access to their medical records. However, radiology reports are written for clinicians and typically contain medical jargon, which can be confusing. One solution is for radiologists to provide a "colloquial" version that is accessible to the layperson. Because manually generating these colloquial translations would represent a significant burden for radiologists, a way to automatically produce accurate, accessible patient-facing reports is desired. We propose a novel method to produce colloquial translations of radiology reports by providing specialized prompts to a large language model (LLM). MATERIALS AND METHODS: Our method automatically extracts and defines medical terms and includes their definitions in the LLM prompt. Using our method and a naive strategy, translations were generated at 4 different reading levels for 100 de-identified neuroradiology reports from an academic medical center. Translations were evaluated by a panel of radiologists for accuracy, likability, harm potential, and readability. RESULTS: Our approach translated the Findings and Impression sections at the 8th-grade level with accuracies of 88% and 93%, respectively. Across all grade levels, our approach was 20% more accurate than the baseline method. Overall, translations were more readable than the original reports, as evaluated using standard readability indices. CONCLUSION: We find that our translations at the eighth-grade level strike an optimal balance between accuracy and readability. Notably, this corresponds to nationally recognized recommendations for patient-facing health communication. We believe that using this approach to draft patient-accessible reports will benefit patients without significantly increasing the burden on radiologists.

Comparing Accuracy of Night Radiology Interpretations for Pediatric Trauma: Radiology Residents Versus Attending Teleradiologists.

Background: Overnight radiology coverage for pediatric trauma patients (PTPs) is addressed with a combination of on-call radiology residents (RRs) and/or attending teleradiologists (ATs); however, the accuracy of these two groups has not been investigated for PTPs. We aimed to compare the accuracy of RRs vs AT interpretations of computed tomography (CT) scans for PTPs. Methods: Pediatric trauma patients (<18 years old) at a single level-I adult/level-II pediatric trauma center were studied in a retrospective analysis (3/2019-5/2020). Computed tomography scans interpreted by both RRs and ATs were included. Radiology residents were compared to ATs for time to interpretation (TTI) and accuracy compared to faculty attending radiologist interpretation, using the validated RADPEER scoring system. Additionally, RR and AT accuracies were compared to a previously studied adult cohort during the same time-period. Results: 42 PTPs (270 interpretations) and 1053 adults (8226 interpretations) were included. Radiology residents had similar rates of discrepancy (13.3% vs 13.3%), major discrepancy (4.4% vs 4.4%), missed findings (9.6% vs 12.6%), and overcalls (3.7% vs .7%) vs ATs (all P > .05). Mean TTI was shorter for RRs (55.9 vs 90.4 minutes, P < .001). Radiology residents had a higher discrepancy rate for PTPs (13.3% vs 7.5%, P = .01) than adults. Attending teleradiologists had a similar discrepancy rate for PTPs and adults (13.3% vs 8.9%, P = .07). Discussion: When interpreting PTP CT imaging, RRs had similar discrepancy rates but faster TTI than ATs. Radiology residents had a higher discrepancy rate for PTP CTs than RR interpretation of adult patients, indicating both RRs and ATs need more focused training in the interpretation of PTP studies.

Automated detection of IVC filters on radiographs with deep convolutional neural networks.

PURPOSE: To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. METHODS: A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. RESULTS: On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%). CONCLUSION: Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.

Novel Retroperitoneal Neovascularity Scoring System in Renal Cell Carcinoma Tumor Staging.

Purpose: Renal cell carcinoma (RCC) is the most common type of kidney cancer worldwide. Although radiologists assess enhancement patterns of renal tumors to predict tumor pathology report, to our knowledge, no formal scoring system has been created and validated to assess the level of neovascularity in RCC, despite its critical role in cancer metastases. In this study, we characterized and analyzed the level of angiogenesis in tumor-burdened kidneys and their benign counterparts. We then created and validated a scoring scale for neovascularity that can help predict tumor staging for RCC. Methods: After Institutional Review Board approval, the charts of patients who had undergone operation for RCC between January 13, 2014 and February 4, 2020 were retrospectively reviewed for inclusion in this study. Inclusion criteria were a diagnosis of RCC, simple/radical nephrectomy, preoperative contrast-enhanced CT scans, and complete pathology reports. Neovascularity was scored on a scale of 0-4 where 0 = no neovascularity detected, 1 = a single vessel <3 mm wide, 2 = a single vessel ≥3 mm wide, 3 = multiple vessels <3 mm wide, and 4 = multiple vessels ≥3 mm wide. Results: A total of 227 patients were included in this study. Most of the tumor pathology reports were clear cell carcinoma, regardless of tumor staging. The average neovascularity score was 1.07 for pT1x tumors, 2.83 for pT2x tumors, and 3.04 for pT3x tumors. There was a significant difference in neovascularity score between pT1x and pT2x tumors (p = 0.0046), pT1x and pT3x tumors (p < 0.0001), and benign kidneys and kidneys with RCC (p < 0.0001). Conclusion: Our novel vascular scoring system for RCC demonstrates significant correlation with RCC pathological tumor staging. This scoring system may be utilized as part of a comprehensive radiological assessment of renal tumors, potentially improving tumor characterization and clinical decision making.

Artificial Intelligence for Improved Hepatosplenomegaly Diagnosis.

Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.

Of model hosts and man: using Caenorhabditis elegans, Drosophila melanogaster and Galleria mellonella as model hosts for infectious disease research.

The use of invertebrate model hosts has increased in popularity due to numerous advantages of invertebrates over mammalian models, including ethical, logistical and budgetary features. This review provides an introduction to three model hosts, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and the larvae of Galleria mellonella, the greater wax moth. It highlights principal experimental advantages of each model, for C. elegans the ability to run high-throughput assays, for D. melanogaster the evolutionarily conserved innate immune response, and for G. mellonella the ability to conduct experiments at 37°C and easily inoculate a precise quantity of pathogen. It additionally discusses recent research that has been conducted with each host to identify pathogen virulence factors, study the immune response, and evaluate potential antimicrobial compounds, focusing principally on fungal pathogens.

Night Radiology Coverage for Trauma: Residents, Teleradiology, or Both?

BACKGROUND: Overnight radiology coverage for trauma patients is often addressed with a combination of on-call radiology residents (RR) and a teleradiology service; however, the accuracy of these 2 readers has not been studied for trauma. We aimed to compare the accuracy of RR versus teleradiologist interpretations of CT scans for trauma patients. STUDY DESIGN: A retrospective analysis (March 2019 through May 2020) of trauma patients presenting to a single American College of Surgeons Level I trauma center was performed. Patients whose CT scans were performed between 10 pm to 8 am were included, because their scans were interpreted by both a RR and teleradiologist. Interpretations were compared with the final attending faculty radiologist's interpretation and graded for accuracy based on the RADPEER scoring system. Discrepancies were characterized as traumatic injury or incidental findings and missed findings or overcalls. Turnaround time was also compared. RESULTS: A total of 1,053 patients and 8,226 interpretations were included. Compared with teleradiologists, RR had a lower discrepancy (7.7% vs 9.0%, p = 0.026) and major discrepancy rate (3.8% vs 5.2%, p = 0.003). Among major discrepancies, RR had a lower rate of traumatic injury discrepancies (3.2% vs 4.4%, p = 0.004) and missed findings (3.4% vs 5.1%, p < 0.001), but a higher rate of overcalls (0.5% vs 0.1%, p < 0.001) compared with teleradiologists. The mean turnaround time was shorter for RR (51.3 vs 78.8 minutes, p < 0.001). The combination of both RR and teleradiologist interpretations had a lower overall discrepancy rate than RR (5.0% vs 7.7%, p < 0.001). CONCLUSIONS: This study identified lower discrepancy rates and a faster turnaround time by RR compared with teleradiologists for trauma CT studies. The combination of both interpreters had an even lower discrepancy rate, suggesting this combination is optimal when an in-house attending radiologist is not available.

A 61-year-old woman with jejunal lymphatic malformation visualized on computed tomography: a case report.

BACKGROUND: Jejunal lymphatic malformations are congenital lesions that are seldom diagnosed in adults and rarely seen on imaging. CASE PRESENTATION: A 61-year-old Caucasian woman was initially diagnosed and treated for mucinous ovarian carcinoma. After an exploratory laparotomy with left salpingo-oophorectomy, a computed tomography scan of the abdomen and pelvis demonstrated suspicious fluid-containing lesions involving a segment of jejunum and adjacent mesentery. Resection of the lesion during subsequent debulking surgery revealed that the lesion seen on imaging was a jejunal lymphatic malformation and not a cancerous implant. CONCLUSIONS: Abdominal lymphatic malformations are difficult to diagnose solely on imaging but should remain on the differential in adult cancer patients with persistent cystic abdominal lesions despite chemotherapy and must be differentiated from metastatic implants.