Space Ultrasound: A Proposal for Competency-based Ultrasound Training for In-flight Space Medicine.

Space travel has transformed in the past several years. Given the burgeoning market for space tourism, in-flight medical emergencies are likely to be expected. Ultrasound is one of the few diagnostic and therapeutic modalities available for astronauts in space. However, while point-of-care ultrasound (POCUS) is available, there is no current standard of training for astronaut preparation. We suggest an organized and structured methodology by which astronauts should best prepare for space with the medical equipment available on board. As technology continues to evolve, the assistance of other artificial intelligence and augmented reality systems are likely to facilitate training and dynamic real-time needs during space emergencies. Summary: As space tourism continues to evolve, an organized methodology for POCUS use is advised to best prepare astronauts for space.

Artificial Intelligence to Determine Fetal Sex.

OBJECTIVE: This proof-of-concept study assessed how confidently an artificial intelligence (AI) model can determine the sex of a fetus from an ultrasound image. STUDY DESIGN: Analysis was performed using 19,212 ultrasound image slices from a high-volume fetal sex determination practice. This dataset was split into a training set (11,769) and test set (7,443). A computer vision model was trained using a transfer learning approach with EfficientNetB4 architecture as base. The performance of the computer vision model was evaluated on the hold out test set. Accuracy, Cohen's Kappa and Multiclass Receiver Operating Characteristic area under the curve (AUC) were used to evaluate the performance of the model. RESULTS: The AI model achieved an Accuracy of 88.27% on the holdout test set and a Cohen's Kappa score 0.843. The ROC AUC score for Male was calculated to be 0.896, for Female a score of 0.897, for Unable to Assess a score of 0.916, and for Text Added a score of 0.981 was achieved. CONCLUSION: This novel AI model proved to have a high rate of fetal sex capture that could be of significant use in areas where ultrasound expertise is not readily available. KEY POINTS: · This is the first proof-of-concept AI model to determine fetal sex.. · This study adds to the growing research in ultrasound AI.. · Our findings demonstrate AI integration into obstetric care..

Popliteal artery aneurysm ultrasound criteria for reporting characteristics.

INTRODUCTION: Duplex ultrasound (DUS) is the modality of choice for surveillance of popliteal artery aneurysms (PAAs). However, noninvasive vascular laboratories have no standard guidelines for reporting results. This study assessed reports of PAA DUS for inclusion of information pertinent to operative decision-making and timing of surveillance. METHODS: This study was a retrospective review of a multi-institutional repository that was queried for all patients with a PAA from 2008 to 2022 and confirmed via manual chart review. DUS reports were abstracted and images were individually annotated for features of interest including dimensions, flow abnormalities, and percent thrombus burden. RESULTS: A total of 166 PAAs in 130 patients had at least one DUS available for viewing. Postoperative surveillance of PAAs was performed at several intervals: the first at 30 months (IQR 3.7-113, n = 44), the second at 64 months (IQR 20-172, n = 31), and the third at 152 months (IQR 46-217, n = 16) after the operation. The largest diameter of operative PAAs (median 27.5 mm, IQR 21.8-38.0) was significantly greater than nonoperative PAAs (median 20.9 mm, IQR 16.7-27.3); p < 0.01. Fewer than 33 (21%) reports commented on patency of distal runoff. We calculated an average percent thrombus of 60% (IQR 19-81) in nonoperative PAAs, which is significantly smaller than 75% (IQR 58-89) in operative PAAs; p < 0.01. CONCLUSION: In this multi-institutional retrospective study, PAAs are often not followed at intervals recommended by the Society for Vascular Surgery guidelines and do not include all measurements necessary for clinical decision-making in the multi-institutional repository studied. There should be standardization of PAA DUS protocols performed by all noninvasive vascular laboratories to ensure completeness of PAA DUS images and inclusion of characteristics pertinent to clinical decision-making in radiology reports.

Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F 1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility.

Application of Point-of-care Ultrasound for Screening Climbers at High Altitude for Pulmonary B-lines.

INTRODUCTION: High-altitude pulmonary edema (HAPE) occurs as a result of rapid ascent to altitude faster than the acclimatization processes of the body. Symptoms can begin at an elevation of 2,500 meters above sea level. Our objective in this study was to determine the prevalence and trend of developing B-lines at 2,745 meters above sea level among healthy visitors over four consecutive days. METHODS: We performed a prospective case series on healthy volunteers at Mammoth Mountain, CA, USA. Subjects underwent pulmonary ultrasound for B-lines over four consecutive days. RESULTS: We enrolled 21 male and 21 female participants. There was an increase in the sum of B-lines at both lung bases from day 1 to day 3, with a subsequent decrease from day 3 to day 4(P<0.001). By the third day at altitude, B-lines were detectable at base of lungs of all participants. Similarly, B-lines increased at apex of lungs from day 1 to day 3 and decreased on day 4 (P=0.004). CONCLUSION: By the third day at 2,745 meters altitude, B-lines were detectable in the bases of both lungs of all healthy participants in our study. We assume that increasing the number of B-lines could be considered an early sign of HAPE. Point-of-care ultrasound could be used to detect and monitor B-lines at altitude to facilitate early detection of HAPE, regardless of pre-existing risk factors.

The Viability of an Artificial Intelligence/Machine Learning Prediction Model to Determine Candidates for Knee Arthroplasty.

BACKGROUND: The purpose of this study is to assess the viability of a knee arthroplasty prediction model using 3-view X-rays that helps determine if patients with knee pain are candidates for total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), or are not arthroplasty candidates. METHODS: Analysis was performed using radiographic and surgical data from a high-volume joint replacement practice. The dataset included 3 different X-ray views (anterior-posterior, lateral, and sunrise) for 2,767 patients along with information of whether that patient underwent an arthroplasty surgery (UKA or TKA) or not. This resulted in a dataset including 8,301 images from 2,707 patients. This dataset was then split into a training set (70%) and holdout test set (30%). A computer vision model was trained using a transfer learning approach. The performance of the computer vision model was evaluated on the holdout test set. Accuracy and multiclass receiver operating characteristic area under curve was used to evaluate the performance of the model. RESULTS: The artificial intelligence model achieved an accuracy of 87.8% on the holdout test set and a quadratic Cohen's kappa score of 0.811. The multiclass receiver operating characteristic area under curve score for TKA was calculated to be 0.97; for UKA a score of 0.96 and for No Surgery a score of 0.98 was achieved. An accuracy of 93.8% was achieved for predicting Surgery versus No Surgery and 88% for TKA versus not TKA was achieved. CONCLUSION: The artificial intelligence/machine learning model demonstrated viability for predicting which patients are candidates for a UKA, TKA, or no surgical intervention.

Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images.

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.

Novice-performed point-of-care ultrasound for home-based imaging.

Patient-performed point-of-care ultrasound (POCUS) may be feasible for use in home-based healthcare. We investigated whether novice users can obtain lung ultrasound (LUS) images via self-scanning with similar interpretability and quality as experts. Adult participants with no prior medical or POCUS training, who were capable of viewing PowerPoint slides in their home and who could hold a probe to their chest were recruited. After training, volunteers self-performed 8-zone LUS and saved images using a hand-held POCUS device in their own home. Each 8-zone LUS scan was repeated by POCUS experts. Clips were independently viewed and scored by POCUS experts blinded to performing sonographers. Quality and interpretability scores of novice- and expert-obtained LUS images were compared. Thirty volunteers with average age of 42.8 years (Standard Deviation (SD) 15.8), and average body mass index of 23.7 (SD 3.1) were recruited. Quality of novice and expert scans did not differ (median score 2.6, interquartile range (IQR) 2.3-2.9 vs. 2.8, IQR 2.3-3.0, respectively p = 0.09). Individual zone quality also did not differ (P > 0.05). Interpretability of LUS was similar between expert and novice scanners (median 7 zones interpretable, IQR 6-8, for both groups, p = 0.42). Interpretability of novice-obtained scans did not differ from expert scans (median 7 out of 8 zones, IQR 6-8, p = 0.42). Novice-users can self-obtain interpretable, expert-quality LUS clips with minimal training. Patient-performed LUS may be feasible for outpatient home monitoring.

Artificial intelligence for renal cancer: From imaging to histology and beyond.

Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%-17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.

The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education.

Radiology education is understood to be an important component of medical school and resident training, yet lacks a standardization of instruction. The lack of uniformity in both how radiology is taught and learned has afforded opportunities for new technologies to intervene. Now with the integration of artificial intelligence within medicine, it is likely that the current medical trainee curricula will experience the impact it has to offer both for education and medical practice. In this paper, we seek to investigate the landscape of radiologic education within the current medical trainee curricula, and also to understand how artificial intelligence may potentially impact the current and future radiologic education model.

Tricuspid Annular Plane of Systolic Excursion (TAPSE) for the Evaluation of Patients with Severe Sepsis and Septic Shock.

INTRODUCTION: Sepsis is a systemic infection that can rapidly progress into multi organ failure and shock if left untreated. Previous studies have demonstrated the utility of point of care ultrasound (POCUS) in the evaluation of patients with sepsis. However, limited data exists on the evaluation of the tricuspid annular plane of systolic excursion (TAPSE) in patients with sepsis. METHODS: We prospectively enrolled patients who presented to the emergency department (ED) with concern for severe sepsis or septic shock in a pilot study. In patients that screened positive, the treating physician then performed POCUS to measure the TAPSE value. We compared the intensive care unit (ICU) admission rate, hospital length of stay, and morbidity with their respective TAPSE values. RESULTS: We enrolled 24 patients in the study. Eight patients had TAPSE values less than 16 millimeters (mm), two patients had TAPSE values between 16mm-20mm, and fourteen patients had TAPSE values greater than 20mm. There was no statistically significant association between TAPSE levels and ICU admission (p=0.16), or death (p=0.14). The difference of length of stay (LOS) was not statistically significant in case of hospital LOS (p= 0.72) or ICU LOS. CONCLUSION: Our pilot data did not demonstrate a correlation between severe sepsis or septic shock and TAPSE values. This may be due to several factors including patient comorbidities, strict definitions of sepsis and septic shock, as well as the absence of septic cardiomyopathy (SCM) in patients with sepsis and septic shock. Future large-scale studies are needed to determine if TAPSE can be beneficial in the ED evaluation of patients with concern for SCM.

Evaluation of a Standardized Cardiac Athletic Screening for National Collegiate Athletic Association (NCAA) Athletes.

INTRODUCTION: Sudden cardiac death is a rare cause of death in young athletes. Current screening techniques include history and physical exam (H and P), with or without an electrocardiogram (ECG). Adding point of care cardiac ultrasound has demonstrated benefits, but there is limited data about implementing this technology. We evaluated the feasibility of adding ultrasound to preparticipation screening for collegiate athletes. METHODS: We prospectively enrolled 42 collegiate athletes randomly selected from several sports. All athletes were screened using a 14-point H and P based on 2014 American College of Cardiology (ACC) and American Heart Association (AHA) guidelines, ECG, and cardiac ultrasound. RESULTS: We screened 11 female and 31 male athletes. On ultrasound, male athletes demonstrated significantly larger interventricular septal wall thickness (p = 0.002), posterior wall thickness (p <0.001) and aortic root breadth (p = 0.002) compared to females. Based on H and P and ECGs alone and a combination of H and P with ECG, no athletes demonstrated a positive screening for cardiac abnormalities. However, with combined H and P, ECG, and cardiac ultrasound, one athlete demonstrated positive findings. CONCLUSIONS: We believe that adding point of care ultrasound to the preparticipation exam of college athletes is feasible. This workflow may provide a model for athletic departments' screening.

Methemoglobinemia Induced By Ingesting Lava Lamp Contents.

A patient presented after ingesting the contents of a lava lamp that he believed to contain alcohol. It was later discovered that this product was comprised of 76% calcium nitrate, leading to his subsequent development of methemoglobinemia. This disease is a medical emergency secondary to poor transportation of oxygen and resultant tissue hypoxic effects. Therefore, having high suspicion for this disease process in patients with toxic ingestions, understanding the proper diagnosis, and promptly starting treatment are all critical actions for emergency physicians.

Point-of-care ultrasound versus radiology department pelvic ultrasound on emergency department length of stay.

BACKGROUND: The study aimed to compare the time to overall length of stay (LOS) for patients who underwent point-of-care ultrasound (POCUS) versus radiology department ultrasound (RDUS). METHODS: This was a prospective study on a convenience sample of patients who required pelvic ultrasound imaging as part of their emergency department (ED) assessment. RESULTS: We enrolled a total of 194 patients who were on average 32 years-old. Ninety-eight (51%) patients were pregnant (<20 weeks). Time to completion of RDUS was 66 minutes longer than POCUS (95%CI 60-73, P<0.01). Patients randomized to the RDUS arm experienced a 120 minute longer ED length of stay (LOS) (95%CI 66-173, P<0.01). CONCLUSION: In patients who require pelvic ultrasound as part of their diagnostic evaluation, POCUS resulted in a significant decrease in time to ultrasound and ED LOS.

Point-of-care ultrasound versus radiology department pelvic ultrasound on emergency department length of stay / 世界急诊医学杂志（英文）

@#BACKGROUND: The study aimed to compare the time to overall length of stay (LOS) for patients who underwent point-of-care ultrasound (POCUS) versus radiology department ultrasound (RDUS).METHODS: This was a prospective study on a convenience sample of patients who required pelvic ultrasound imaging as part of their emergency department (ED) assessment.RESULTS: We enrolled a total of 194 patients who were on average 32 years-old. Ninety-eight (51％) patients were pregnant (<20 weeks). Time to completion of RDUS was 66 minutes longer than POCUS (95％CI 60–73,P<0.01). Patients randomized to the RDUS arm experienced a 120 minute longer ED length of stay (LOS) (95％CI 66–173,P<0.01)CONCLUSION: In patients who require pelvic ultrasound as part of their diagnostic evaluation, POCUS resulted in a signifi cant decrease in time to ultrasound and ED LOS.

Differential diagnosis of cardiovascular symptoms: setting the expectations for the ultrasound examination and medical education.

Within the past several decades, dramatic changes have been made in the field of diagnostic imaging. Many of these changes have been with ultrasound, which has been transformative in the efficiency and accuracy of diagnostics. Emergency physicians, intensivists, and other acute care clinicians are using and relying on critical care ultrasound imaging to better triage and diagnose patients at the point of care. As this new frontier of medicine continues to forge forward using this new and improving technology, we strongly believe in integrating ultrasound training earlier into the medical education curriculum. This paper reviews and discusses the transformation of medical diagnostics within the last few decades and describes changes that should be expected as point-of-care cardiac ultrasound evolves within medical education.