Artificial intelligence in medical ultrasonography: driving on an unpaved road

ChatGPT-4-Driven Liver Ultrasound Radiomics Analysis: Advantages and Drawbacks Compared to Traditional Techniques

BACKGROUND

Artificial intelligence (AI) is transforming medical imaging, with large language models such as ChatGPT-4 emerging as potential tools for automated image interpretation. While AI-driven radiomics has shown promise in diagnostic imaging, the efficacy of ChatGPT-4 in liver ultrasound analysis remains largely unexamined.

OBJECTIVE

This study evaluates the capability of ChatGPT-4 in liver ultrasound radiomics, specifically its ability to differentiate fibrosis, steatosis, and normal liver tissue, compared to conventional image analysis software.

METHODS

Seventy grayscale ultrasound images from a preclinical liver disease model, including fibrosis (n=31), fatty liver (n=18), and normal liver (n=21), were analyzed. ChatGPT-4 extracted texture features, which were compared to those obtained using Interactive Data Language (IDL), a traditional image analysis software. One-way ANOVA was used to identify statistically significant features differentiating liver conditions, and logistic regression models were employed to assess diagnostic performance.

RESULTS

ChatGPT-4 extracted nine key textural features-echo intensity, heterogeneity, skewness, kurtosis, contrast, homogeneity, dissimilarity, angular second moment, and entropy-all of which significantly differed across liver conditions (p < 0.05). Among individual features, echo intensity achieved the highest F1-score (0.85). When combined, ChatGPT-4 attained 76% accuracy and 83% sensitivity in classifying liver disease. ROC analysis demonstrated strong discriminatory performance, with AUC values of 0.75 for fibrosis, 0.87 for normal liver, and 0.97 for steatosis. Compared to Interactive Data Language (IDL) image analysis software, ChatGPT-4 exhibited slightly lower sensitivity (0.83 vs. 0.89) but showed moderate correlation (R = 0.68, p < 0.0001) with IDL-derived features. However, it significantly outperformed IDL in processing efficiency, reducing analysis time by 40%, highlighting its potential for high throughput radiomic analysis.

CONCLUSIONS

Despite slightly lower sensitivity than IDL, ChatGPT-4 demonstrated high feasibility for ultrasound radiomics, offering faster processing, high-throughput analysis, and automated multi-image evaluation. These findings support its potential integration into AI-driven imaging workflows, with further refinements needed to enhance feature reproducibility and diagnostic accuracy.

CLINICALTRIAL

A Comparative Bicentric Study on Ultrasound Education for Students: App- and AI-Supported Learning Versus Traditional Hands-on Instruction (AI-Teach Study)

BACKGROUND

The integration of artificial intelligence (AI) into medical education presents significant opportunities for enhancing teaching methods and student learning outcomes. Despite its potential benefits, the implementation of AI in curricula remains limited and lacks standardized approaches.

OBJECTIVE

This bicentric pilot study aims to examine the effectiveness of an innovative ultrasound course for medical students that combines AI-based teaching with blended e-learning, compared to traditional classroom lessons, to optimize educational practices.

MATERIAL AND METHODS

This bicentric pilot study included medical students who were randomly assigned to an experimental group receiving AI-based blended e-learning for an ultrasound course or a control group receiving traditional classroom instruction. The curriculum consisted of two modules: lung ultrasound and Focused Assessment with Sonography for Trauma (FAST). The effectiveness of the interventions was evaluated using objective structured clinical examinations (OSCE) to assess ultrasound skills, administered as pre-tests and post-tests. Additionally, the quality of the ultrasound images obtained during the final assessment was rated using a standardized scoring system to further assess student competency.

RESULTS

50 clinical-phase medical students participated. OSCE results for both FAST and lung modules revealed no significant differences between groups at both pretest (pretest FAST p=0.722, pretest Lung p=0.062) and final exam (final exam FAST p=0.634, final exam lung p=0.843), with both cohorts achieving comparable improvements and nearly identical final scores, while ultrasound image evaluations confirmed similar outcomes (FAST images p=0.558 and lung images p=0.199) with excellent interrater reliability (ICC=0.993).

CONCLUSION

AI- and app-based learning methods in ultrasound education showed to be equally effective as traditional hands-on teaching for medical students in this study. Incorporating the permanently growing innovations auf AI into curricula can provide valuable tools for educators and students alike.

Overcoming barriers in the use of artificial intelligence in point of care ultrasound

Point-of-care ultrasound is a portable, low-cost imaging technology focused on answering specific clinical questions in real time. Artificial intelligence amplifies its capabilities by aiding clinicians in the acquisition and interpretation of the images; however, there are growing concerns on its effectiveness and trustworthiness. Here, we address key issues such as population bias, explainability and training of artificial intelligence in this field and propose approaches to ensure clinical effectiveness.

The Transformative Potential of AI in Ultrasound for Facial Aesthetics

BACKGROUND

The integration of artificial intelligence (AI) and ultrasound (US) technology is reshaping facial aesthetics, providing enhanced diagnostic precision, procedural safety, and personalized patient care. The variability in US imaging, stemming from patient anatomy, operator skills, and equipment diversity, poses challenges in achieving consistent and accurate outcomes. AI addresses these limitations by standardizing imaging protocols, automating image analysis, and supporting real-time decision-making.

OBJECTIVE

To explore the applications of AI-enhanced US in facial aesthetics, focusing on its potential to improve diagnostic accuracy, procedural safety, and personalized treatments while identifying future prospects and challenges.

METHODS

A comprehensive review of current literature and advancements was conducted, examining the integration of AI with US in facial aesthetics. Key areas of focus included AI algorithms for image enhancement, real-time guidance during procedures, postprocedure assessment, personalized treatment planning, and workflow optimization.

RESULTS

AI-enhanced US significantly improved diagnostic accuracy by automating the identification of critical anatomical structures and reducing operator variability. Real-time guidance during procedures enhanced safety, reducing complications such as vascular occlusion and nerve damage. Postprocedure assessments facilitated early detection of complications and improved patient outcomes. Personalized treatment plans tailored to individual anatomy and clinical needs resulted in higher patient satisfaction. Additionally, AI optimized workflow efficiency through seamless integration with electronic health records and advanced training simulators.

CONCLUSION

The integration of AI and US technology represents a transformative advancement in facial aesthetics. By enhancing precision, safety, and personalization, AI-powered US sets new benchmarks in diagnostic accuracy and treatment outcomes. Despite challenges related to data diversity, ethical considerations, and training, this synergy holds immense potential to revolutionize the field, offering improved outcomes and satisfaction for practitioners and patients alike. Further research and innovation are essential to fully realize the benefits of this technology.

Anticipating impending peripheral intravenous catheter failure: A diagnostic accuracy observational study combining ultrasound and artificial intelligence to improve clinical care

OBJECTIVE

Peripheral intravenous catheter (PIVC) failure occurs in approximately 50% of insertions. Unexpected PIVC failure leads to treatment delays, longer hospitalizations, and increased risk of patient harm. In current practice there is no method to predict if PIVC failure will occur until it is too late and a grossly obvious complication has occurred. The aim of this study is to demonstrate the diagnostic accuracy of a predictive model for PIVC failure based on artificial intelligence (AI).

METHODS

This study evaluated the capabilities of a novel machine learning algorithm. The algorithm was trained using real-world ultrasound videos of PIVC sites with a goal of predicting which PIVCs would fail within the following day. After training, AI models were validated using another, unseen, collection of real-world ultrasound videos of PIVC sites.

RESULTS

2133 ultrasound videos (361 failure and 1772 non-failure) were used for algorithm development. When the algorithm was tasked with predicting failure in the unseen collection of videos, the best achieved results were an accuracy of 0.93, sensitivity of 0.77, specificity of 0.98, positive predictive value of 0.91, negative predictive value of 0.93, and area under the curve of 0.87.

CONCLUSIONS

This proprietary and novel machine learning algorithm can accurately and reliably predict PIVC failure 1 day prior to clinically evident failure. Implementation of this technology in the patient care setting would provide timely information for clinicians to plan and manage impending device failure. Future research on the use of AI technology and PIVCs should focus on improving catheter function and longevity, while limiting complication rates.

Machine Learning and Deep Learning Applications in Magnetic Particle Imaging

In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution achieved by using relaxometry in order to measure the magnetic fields. MPI presents 2D and 3D images with high temporal resolution, non-ionizing radiation, and optimal visual contrast due to its lack of background tissue signal. Traditionally, the images were reconstructed by the conversion of signal from the induced voltage by generating system matrix and X-space based methods. Because image reconstruction and analyses play an integral role in obtaining precise information from MPI signals, newer artificial intelligence-based methods are continuously being researched and developed upon. In this work, we summarize and review the significance and employment of machine learning and deep learning models for applications with MPI and the potential they hold for the future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

EUS in children with eosinophilic oesophagitis - a new method of measuring oesophageal total wall thickness area. An artificial intelligence application feasibility study. A pilot study

Aim

In the study, we aimed to introduce a formula for measuring the oesophageal total wall thickness area, which could be used for developing an artificial intelligence-based algorithm for the detection of patients whose total wall thickness area exceeds the norms.

Material and methods

Mathematical formulas for measuring the square area of the oesophageal total wall thickness area were introduced and applied. Children were grouped according to their weight in clusters. For each cluster, the range (minimal and maximal value) were established. The measurements were done by using the formula for the area of the circular ring according to the formula A = n (B2-b2); the product of n and subtraction square b (smaller radius) and square B (bigger radius). The basic data for our calculations were derived from papers published by Dalby et al., 2010 and Loff et al., 2022.

Results

The square area (in mm2) of the oesophageal wall was calculated and proposed to be introduced for further analysis. This value set could be used for creating an algorithm for computer-aided analysis of patients diagnosed with sonographic examination and isolating patients for surveillance. Our newly introduced approach could be implemented in sonographic, computer tomography, and magnetic resonance examinations in eosinophilic oesophagitis and other oesophageal diseases.

Conclusions

Total wall thickness area could be used for monitoring children with eosinophilic oesophagitis and other oesophageal diseases. The method could also be applied for adults. Therefore, it can be a foundation for further progress with applying artificial intelligence algorithms.

Student ultrasound education, current view and controversies. Role of Artificial Intelligence, Virtual Reality and telemedicine

The digitization of medicine will play an increasingly significant role in future years. In particular, telemedicine, Virtual Reality (VR) and innovative Artificial Intelligence (AI) systems offer tremendous potential in imaging diagnostics and are expected to shape ultrasound diagnostics and teaching significantly. However, it is crucial to consider the advantages and disadvantages of employing these new technologies and how best to teach and manage their use. This paper provides an overview of telemedicine, VR and AI in student ultrasound education, presenting current perspectives and controversies.

Advancements in Interventional Pulmonology: Harnessing Ultrasound Techniques for Precision Diagnosis and Treatment

Medical ultrasound has emerged as an indispensable tool within interventional pulmonology, revolutionizing diagnostic and procedural practices through its non-invasive nature and real-time visualization capabilities. By harnessing the principles of sound waves and employing a variety of transducer types, ultrasound facilitates enhanced accuracy and safety in procedures such as transthoracic needle aspiration and pleural effusion drainage, consequently leading to improved patient outcomes. Understanding the fundamentals of ultrasound physics is paramount for clinicians, as it forms the basis for interpreting imaging results and optimizing interventions. Thoracic ultrasound plays a pivotal role in diagnosing conditions like pleural effusions and pneumothorax, while also optimizing procedures such as thoracentesis and biopsy by providing precise guidance. Advanced ultrasound techniques, including endobronchial ultrasound, has transformed the evaluation and biopsy of lymph nodes, bolstered by innovative features like elastography, which contribute to increased procedural efficacy and patient safety. Peripheral ultrasound techniques, notably radial endobronchial ultrasound (rEBUS), have become essential for assessing pulmonary nodules and evaluating airway structures, offering clinicians valuable insights into disease localization and severity. Neck ultrasound serves as a crucial tool in guiding supraclavicular lymph node biopsy and percutaneous dilatational tracheostomy procedures, ensuring safe placement and minimizing associated complications. Ultrasound technology is suited for further advancement through the integration of artificial intelligence, miniaturization, and the development of portable devices. These advancements hold the promise of not only improving diagnostic accuracy but also enhancing the accessibility of ultrasound imaging in diverse healthcare settings, ultimately expanding its utility and impact on patient care. Additionally, the integration of enhanced techniques such as contrast-enhanced ultrasound and 3D imaging is anticipated to revolutionize personalized medicine by providing clinicians with a more comprehensive understanding of anatomical structures and pathological processes. The transformative potential of medical ultrasound in interventional pulmonology extends beyond mere technological advancements; it represents a paradigm shift in healthcare delivery, empowering clinicians with unprecedented capabilities to diagnose and treat pulmonary conditions with precision and efficacy. By leveraging the latest innovations in ultrasound technology, clinicians can navigate complex anatomical structures with confidence, leading to more informed decision-making and ultimately improving patient outcomes. Moreover, the portability and versatility of modern ultrasound devices enable their deployment in various clinical settings, from traditional hospital environments to remote or resource-limited areas, thereby bridging gaps in healthcare access and equity.

Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: AJR Expert Panel Narrative Review

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging tasks, such as disease diagnosis and image reconstruction. AI applications in musculoskeletal imaging have focused primarily on radiography, CT, and MRI. Although musculoskeletal ultrasound stands to benefit from AI in similar ways, such applications have been relatively underdeveloped. In comparison with other modalities, ultrasound has unique advantages and disadvantages that must be considered in AI algorithm development and clinical translation. Challenges in developing AI for musculoskeletal ultrasound involve both clinical aspects of image acquisition and practical limitations in image processing and annotation. Solutions from other radiology subspecialties (e.g., crowdsourced annotations coordinated by professional societies), along with use cases (most commonly rotator cuff tendon tears and palpable soft-tissue masses), can be applied to musculoskeletal ultrasound to help develop AI. To facilitate creation of high-quality imaging datasets for AI model development, technologists and radiologists should focus on increasing uniformity in musculoskeletal ultrasound performance and increasing annotations of images for specific anatomic regions. This Expert Panel Narrative Review summarizes available evidence regarding AI's potential utility in musculoskeletal ultrasound and challenges facing its development. Recommendations for future AI advancement and clinical translation in musculoskeletal ultrasound are discussed.

Automated classification of liver fibrosis stages using ultrasound imaging

BACKGROUND

Ultrasound imaging is the most frequently performed for the patients with chronic hepatitis or liver cirrhosis. However, ultrasound imaging is highly operator dependent and interpretation of ultrasound images is subjective, thus well-trained radiologist is required for evaluation. Automated classification of liver fibrosis could alleviate the shortage of skilled radiologist especially in low-to-middle income countries. The purposed of this study is to evaluate deep convolutional neural networks (DCNNs) for classifying the degree of liver fibrosis according to the METAVIR score using US images.

METHODS

We used ultrasound (US) images from two tertiary university hospitals. A total of 7920 US images from 933 patients were used for training/validation of DCNNs. All patient were underwent liver biopsy or hepatectomy, and liver fibrosis was categorized based on pathology results using the METAVIR score. Five well-established DCNNs (VGGNet, ResNet, DenseNet, EfficientNet and ViT) was implemented to predict the METAVIR score. The performance of DCNNs for five-level (F0/F1/F2/F3/F4) classification was evaluated through area under the receiver operating characteristic curve (AUC) with 95% confidential interval, accuracy, sensitivity, specificity, positive and negative likelihood ratio.

RESULTS

Similar mean AUC values were achieved for five models; VGGNet (0.96), ResNet (0.96), DenseNet (0.95), EfficientNet (0.96), and ViT (0.95). The same mean accuracy (0.94) and specificity values (0.96) were yielded for all models. In terms of sensitivity, EffcientNet achieved highest mean value (0.85) while the other models produced slightly lower values range from 0.82 to 0.84.

CONCLUSION

In this study, we demonstrated that DCNNs can classify the staging of liver fibrosis according to METAVIR score with high performance using conventional B-mode images. Among them, EfficientNET that have fewer parameters and computation cost produced highest performance. From the results, we believe that DCNNs based classification of liver fibrosis may allow fast and accurate diagnosis of liver fibrosis without needs of additional equipment for add-on test and may be powerful tool for supporting radiologists in clinical practice.

Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis

Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.

Feasibility of a deep learning artificial intelligence model for the diagnosis of pediatric ileocolic intussusception with grayscale ultrasonography

PURPOSE

This study explored the feasibility of utilizing a deep learning artificial intelligence (AI) model to detect ileocolic intussusception on grayscale ultrasound images.

METHODS

This retrospective observational study incorporated ultrasound images of children who underwent emergency ultrasonography for suspected ileocolic intussusception. After excluding video clips, Doppler images, and annotated images, 40,765 images from two tertiary hospitals were included (positive-to-negative ratio: hospital A, 2,775:35,373; hospital B, 140:2,477). Images from hospital A were split into a training set, a tuning set, and an internal test set (ITS) at a ratio of 7:1.5:1.5. Images from hospital B comprised an external test set (ETS). For each image indicating intussusception, two radiologists provided a bounding box as the ground-truth label. If intussusception was suspected in the input image, the model generated a bounding box with a confidence score (0-1) at the estimated lesion location. Average precision (AP) was used to evaluate overall model performance. The performance of practical thresholds for the modelgenerated confidence score, as determined from the ITS, was verified using the ETS.

RESULTS

The AP values for the ITS and ETS were 0.952 and 0.936, respectively. Two confidence thresholds, CTopt and CTprecision, were set at 0.557 and 0.790, respectively. For the ETS, the perimage precision and recall were 95.7% and 80.0% with CTopt, and 98.4% and 44.3% with CTprecision. For per-patient diagnosis, the sensitivity and specificity were 100.0% and 97.1% with CTopt, and 100.0% and 99.0% with CTprecision. The average number of false positives per patient was 0.04 with CTopt and 0.01 for CTprecision.

CONCLUSION

The feasibility of using an AI model to diagnose ileocolic intussusception on ultrasonography was demonstrated. However, further study involving bias-free data is warranted for robust clinical validation.

Applications of artificial intelligence in musculoskeletal ultrasound: narrative review

Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner's experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice.

A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology

The exponential growth of artificial intelligence (AI) has fascinated its application in various fields and so in the field of healthcare. Technological advancements in theories and learning algorithms and the availability of processing through huge datasets have created a breakthrough in the medical field with computing systems. AI can potentially drive clinicians and practitioners with appropriate decisions in managing cases and reaching a diagnosis, so its application is extensively spread in the medical field. Thus, computerized algorithms have made predictions so simple and accurate. This is because AI can proffer information accurately even to many patients. Furthermore, the subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. Despite numerous challenges, AI implementation in obstetrics and gynecology is found to have a spellbound development. Therefore, this review propounds exploring the implementation of AI in obstetrics and gynecology to improve the outcomes and clinical experience. In that context, the evolution and progress of AI, the role of AI in ultrasound diagnosis in distinct phases of pregnancy, clinical benefits, preterm birth postpartum period, and applications of AI in gynecology are elucidated in this review with future recommendations.

The application of artificial intelligence in the sonography profession: Professional and educational considerations

The integration of artificial intelligence (AI) technology within the health industry is increasing. This educational piece discusses the implementation of AI and its impact on sonography. The authors investigate how AI may influence the profession and provide examples of how ultrasound imaging may be enhanced and innovated by integrating AI technology. This article highlights challenges related to the application of AI and provides insight into how they could be addressed. The critical distinction between the role of a sonographer and the reporting specialist in the context of AI is highlighted as a key issue for those developing, researching, and evaluating AI systems. A key recommendation is for the sonography community to address ultrasound education, particularly how AI knowledge could be incorporated into university education. This is an important consideration that should be extended to practising professionals as they may be involved in evaluating the efficiency and methodologies used in new research that may incorporate AI technologies.

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

Machine Learning-Aided Chronic Kidney Disease Diagnosis Based on Ultrasound Imaging Integrated with Computer-Extracted Measurable Features

Although ultrasound plays an important role in the diagnosis of chronic kidney disease (CKD), image interpretation requires extensive training. High operator variability and limited image quality control of ultrasound images have made the application of computer-aided diagnosis (CAD) challenging. This study assessed the effect of integrating computer-extracted measurable features with the convolutional neural network (CNN) on the ultrasound image CAD accuracy of CKD. Ultrasound images from patients who visited Severance Hospital and Gangnam Severance Hospital in South Korea between 2011 and 2018 were used. A Mask regional CNN model was used for organ segmentation and measurable feature extraction. Data on kidney length and kidney-to-liver echogenicity ratio were extracted. The ResNet18 model classified kidney ultrasound images into CKD and non-CKD. Experiments were conducted with and without the input of the measurable feature data. The performance of each model was evaluated using the area under the receiver operating characteristic curve (AUROC). A total of 909 patients (mean age, 51.4 ± 19.3 years; 414 [49.5%] men and 495 [54.5%] women) were included in the study. The average AUROC from the model trained using ultrasound images achieved a level of 0.81. Image training with the integration of automatically extracted kidney length and echogenicity features revealed an improved average AUROC of 0.88. This value further increased to 0.91 when the clinical information of underlying diabetes was also included in the model trained with CNN and measurable features. The automated step-wise machine learning-aided model segmented, measured, and classified the kidney ultrasound images with high performance. The integration of computer-extracted measurable features into the machine learning model may improve CKD classification.

Point-of-Care Ultrasound for the Trauma Anesthesiologist

Purpose of Review

With advances in technology and availability of handheld ultrasound probes, studies are focusing on the perioperative care of patients, but a limited number specifically on trauma patients. This review highlights recent findings from studies using point of care ultrasound (POCUS) to improve the care of trauma patients.

Recent Findings

Major findings include the use of POCUS to assess volume status of trauma patients upon arrival to measure the major vasculature. Additionally, several studies have advanced the use of POCUS to identify pneumothorax in trauma patients. Finally, the ASA POCUS certification and ASRA expert guidelines are examples of international organizations establishing guidelines for utilization and training of anesthesiologists in the field of POCUS, which will be discussed.

Summary

Despite the COVID-19 pandemic, and considerable resources being diverted to fight this global healthcare crisis, advances are being made in utilization of POCUS to aid the care of trauma patients.