Ultrasound Interpretation Research Articles

Volume sweep imaging lung teleultrasound for detection of COVID-19 in Peru: a multicentre pilot study

ObjectivesPulmonary disease is a significant cause of morbidity and mortality in adults and children, but most of the world lacks diagnostic imaging for its assessment. Lung ultrasound is a portable,...

Impact of Use of Intersocietal Accreditation Commission Versus Society of Radiologists in Ultrasound Carotid Interpretation Criteria on Patient Management

The Intersocietal Accreditation Commission (IAC) recently introduced modified criteria for carotid duplex ultrasound interpretation. The most significant change, compared with the Society of Radiologists in Ultrasound (SRU) criteria, involved redefining the ranges of <50% and 50% to 69%. The definitions of 70% to 99%, near occlusion, and occlusion remained unchanged. Patients identified in the <50% vs 50% to 69% ranges could experience different disease severity assignments, surveillance protocols, medical management approaches, and access to intervention (if symptomatic).

Machine Learning Classification of Doppler Waveforms

Arterial duplex ultrasound is a noninvasive and readily available imaging study that plays an important role in the diagnosis and surveillance of peripheral artery disease. A component of the duplex ultrasound interpretation includes classification of the Doppler waveforms into triphasic, biphasic, monophasic, and occluded, with the latter two considered abnormal. We sought to use machine learning techniques to automate the classification of the Doppler waveforms.

Evaluation of an Object Detection Algorithm for Shrapnel and Development of a Triage Tool to Determine Injury Severity

Emergency medicine in austere environments rely on ultrasound imaging as an essential diagnostic tool. Without extensive training, identifying abnormalities such as shrapnel embedded in tissue, is challenging. Medical professionals with appropriate expertise are limited in resource-constrained environments. Incorporating artificial intelligence models to aid the interpretation can reduce the skill gap, enabling identification of shrapnel, and its proximity to important anatomical features for improved medical treatment. Here, we apply a deep learning object detection framework, YOLOv3, for shrapnel detection in various sizes and locations with respect to a neurovascular bundle. Ultrasound images were collected in a tissue phantom containing shrapnel, vein, artery, and nerve features. The YOLOv3 framework, classifies the object types and identifies the location. In the testing dataset, the model was successful at identifying each object class, with a mean Intersection over Union and average precision of 0.73 and 0.94, respectively. Furthermore, a triage tool was developed to quantify shrapnel distance from neurovascular features that could notify the end user when a proximity threshold is surpassed, and, thus, may warrant evacuation or surgical intervention. Overall, object detection models such as this will be vital to compensate for lack of expertise in ultrasound interpretation, increasing its availability for emergency and military medicine.

Differentiating non-lactating mastitis and malignant breast tumors by deep-learning based AI automatic classification system: A preliminary study.

ObjectiveTo explore the application values of deep-learning based artificial intelligence (AI) automatic classification system, on the differential diagnosis of non-lactating mastitis (NLM) and malignant breast tumors, via its comparation with traditional ultrasound interpretations and the following interpretation conclusions made by the sonographers with various seniorities.MethodsA total of 707 patients suffering from breast lesions (475 malignant breast tumors and 232 NLM), were selected from the following three medical centers, including Zhejiang Cancer Hospital, Hebei Province Hospital of Traditional Chinese Medicine, and Yantai Affiliated Hospital of Binzhou Medical University, and the time period was set from April 2020 to September 2021. All selected cases firstly accepted the routine breast ultrasound diagnosis, followed by the interpretations from a senior sonographer with more than 15 years of work experience, and an intermediate-aged sonographer with more than 5 years of work experience, independently. Meanwhile, a third physician also interpreted the same ultrasound images by deep learning–based AI automatic classification system, independent of the interpretation results from the previous two physicians. The kappa test was performed to evaluate the consistency between the conventional ultrasound interpretation results and pathological results interpreted from physicians with different working experiences.ResultsIn total, 475 cases of malignant breast tumors (512 nodules) and 232 cases of NLM (255 nodules) were pathologically diagnosed. The accuracy, sensitivity, and specificity of conventional ultrasound interpretations vary from different sonographers with different working experiences. The accuracy, sensitivity, and specificity for intermediate-aged sonographers and senior sonographers were 76.92% (590/767), 84.71% (216/255), and 73.95% (374/512) and 87.35% (670/767), 86.27% (220/255), and 87.89% (450/512), respectively (P<0.001). In contrast, if the threshold was set as 0.5, the accuracy, sensitivity, and specificity from deep learning–based AI automatic classification system were 83.00%, 87.20%, and 85.33%, separately, and the area under the curve was 92.6. The results of the kappa consistency test indicated that the diagnosis results from the image interpretations by senior physicians and deep-learning based AI automatic classification system showed high consistency with postoperative pathological diagnosis results, and the kappa values are 0.72 and 0.71, respectively, with the P-value of less than 0.001. In contrast, the consistency between the image interpretation results from intermediate-aged physicians with less working experience, and postoperative pathological diagnosis results, seemed to be relatively lower, with a kappa value of only 0.53 and P-value of less than 0.001.ConclusionsThe deep learning–based AI automatic classification system is expected to become a reliable auxiliary way to distinguish NLM and malignant breast tumors due to its high sensitivity, accuracy, and specificity.

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

BackgroundUltrasound-guided regional anaesthesia relies on the visualisation of key landmark, target, and safety structures on ultrasound. However, this can be challenging, particularly for inexperienced practitioners. Artificial intelligence (AI) is increasingly being applied to medical image interpretation, including ultrasound. In this exploratory study, we evaluated ultrasound scanning performance by non-experts in ultrasound-guided regional anaesthesia, with and without the use of an assistive AI device. MethodsTwenty-one anaesthetists, all non-experts in ultrasound-guided regional anaesthesia, underwent a standardised teaching session in ultrasound scanning for six peripheral nerve blocks. All then performed a scan for each block; half of the scans were performed with AI assistance and half without. Experts assessed acquisition of the correct block view and correct identification of sono-anatomical structures on each view. Participants reported scan confidence, experts provided a global rating score of scan performance, and scans were timed. ResultsExperts assessed 126 ultrasound scans. Participants acquired the correct block view in 56/62 (90.3%) scans with the device compared with 47/62 (75.1%) without (P=0.031, two data points lost). Correct identification of sono-anatomical structures on the view was 188/212 (88.8%) with the device compared with 161/208 (77.4%) without (P=0.002). There was no significant overall difference in participant confidence, expert global performance score, or scan time. ConclusionsUse of an assistive AI device was associated with improved ultrasound image acquisition and interpretation. Such technology holds potential to augment performance of ultrasound scanning for regional anaesthesia by non-experts, potentially expanding patient access to these techniques. Clinical trial registrationNCT05156099.

Assessment of changes in gaze patterns during training in point-of-care ultrasound

BackgroundPoint-of-care ultrasound (POCUS) is a core skill in emergency medicine (EM), however, there is a lack of objective competency measures. Eye-tracking technology is a potentially useful assessment tool, as gaze patterns can reliably discriminate between experts and novices across medical specialties. We aim to determine if gaze metrics change in an independent and predictable manner during ultrasound training.MethodsA convenience sample of first-year residents from a single academic emergency department was recruited. Participants interpreted 16 ultrasound videos of the focused assessment with sonography for trauma (FAST) scan while their gaze patterns were recorded using a commercially available eye-tracking device. The intervention group then completed an introductory ultrasound course whereas the control group received no additional education. The gaze assessment was subsequently repeated. The primary outcome was total gaze duration on the area of interest (AOI). Secondary outcomes included time to fixation, mean duration of first fixation and mean number of fixations on the AOI.Results10 EM residents in the intervention group and 10 non-EM residents in the control group completed the study. After training, there was an 8.8 s increase in the total gaze time on the AOI in the intervention group compared to a 4.0 s decrease in the control group (p = .03). EM residents were also 3.8 s quicker to fixate on the AOI whereas the control group became 2.5 s slower (p = .04). There were no significant interactions on the number of fixations (0.43 vs. 0.18, p = .65) or duration of first fixation on the AOI (0.02 s vs. 0.06 s, p = .63).ConclusionsThere are significant and quantifiable changes in gaze metrics, which occur with incremental learning after an ultrasound course. Further research is needed to validate the serial use of eye-tracking technology in following a learner’s progress toward competency in point-of-care ultrasound image interpretation.

Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study

BackgroundUltrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure. MethodsUltrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure. ResultsThe artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9–86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0–1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720). ConclusionsArtificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice. Clinical trial registrationNCT04906018.

Evaluation of physician performance using a concurrent-read artificial intelligence system to support breast ultrasound interpretation

PurposeThe purpose of this study was to compare the diagnostic performance and the interpretation time of breast ultrasound examination between reading without and with the artificial intelligence (AI) system as a concurrent reading aid. Material and methodsA fully crossed multi-reader and multi-case (MRMC) reader study was conducted. Sixteen participating physicians were recruited and retrospectively interpreted 172 breast ultrasound cases in two reading scenarios, once without and once with the AI system (BU-CAD™, TaiHao Medical Inc.) assistance for concurrent reading. Interpretations of any given case set with and without the AI system were separated by at least 5 weeks. These reading results were compared to the reference standard and the area under the LROC curve (AUCLROC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for performance evaluations. The interpretation time was also compared between the unaided and aided scenarios. ResultsWith the help of the AI system, the readers had higher diagnostic performance with an increase in the average AUCLROC from 0.7582 to 0.8294 with statistically significant. The sensitivity, specificity, PPV, and NPV were also improved from 95.77%, 24.07%, 44.18%, and 93.50%–98.17%, 30.67%, 46.91%, and 96.10%, respectively. Of these, the improvement in specificity reached statistical significance. The average interpretation time was significantly reduced by approximately 40% when the readers were assisted by the AI system. ConclusionThe concurrent-read AI system improves the diagnostic performance in detecting and diagnosing breast lesions on breast ultrasound images. In addition, the interpretation time is effectively reduced for the interpreting physicians.

P-404 Usability, accuracy and cost-effectiveness of “eDiagEPU”, a medical software for early pregnancies: a retrospective study

Abstract Study question Can early pregnancies be accurately and cost-effectively diagnosed and managed using a new medical computerised tool, named “eDiagEPU”? Summary answer Compared to the standard clinical approach, the retrospective implementation of “eDiagEPU” in a gynaecological emergency unit was correlated with sharper diagnoses and more cost-effective managements. What is known already Early pregnancies complications are responsible for a large percentage of consultations, mostly in emergency units. Moreover, clinical guidelines updates for the management of Intrauterine Pregnancies of Uncertain Viability (IPUV) have become increasingly complex and seem to be unknown or misunderstood by several practitioners. Specifically, a recently published prospective multinational survey revealed a limited knowledge regarding early pregnancy guidelines, with 69.0% of the participants reporting incorrect managements of IPUV and 86.6% misinterpreting the evolution of serum human chorionic gonadotropin (hCG). In an attempt to aid practitioners with the diagnosis and management of early pregnancies, a software, named “eDiagEPU”, was developed. Study design, size, duration A total of 780 consultations, recorded between November 2018 and June 2019 in the gynaecological emergency unit of a tertiary university hospital, were retrospectively encoded in eDiagEPU. Positive hCG, ultrasonographical visualisation of gestational sac or/and embryo corresponding to a gestational age of 14 weeks gestation or less were the inclusion criteria. Diagnoses and managements suggested by eDiagEPU are named “eDiagnoses”. The ones provided by a gynaecologist member of the emergency department staff are called “medDiagnoses”. Participants/materials, setting, methods Identical eDiagnosis and medDiagnosis were considered as correct (gold standard). During follow-up examinations, if they became both identical to a previous discrepant eDiagnosis or medDiagnosis, this previous eDiagnosis/medDiagnosis was considered as correct. Persistent discrepancies were reviewed by four double-blinded experts whose majority defined the correct eDiagnosis/medDiagnosis. The accuracies of eDiagnoses/medDiagnoses were compared using McNemar’s Chi square test, computing diagnostic values (Sensitivity, Specificity, and predictive values) and 95% Confidence Intervals (CI). Cost reduction was also analysed. Main results and the role of chance Only one datum (0.1%) from 780 registered medical records was missing to process using “eDiagEPU”. Out of the 779 consultations that could be fully encoded until obtaining an eDiagnosis, 675 eDiagnoses were identical to the medDiagnoses (86.6%) and 104 discrepant (13.4%). From these 104, 60 reached an agreement during follow-up controls with 59 medDiagnoses finally changing into the initial eDiagnoses (98%) while only one discrepant eDiagnosis turning later into the initial medDiagnosis (2%). Finally, 24 remained discrepant at all subsequent checks and 20 were not reevaluated. Out of these 44 discrepancies without identical diagnoses/managements during follow-up controls, the double-blinded experts majority chose 38 eDiagnoses (86%) and 5 medDiagnoses (11%) including 4 twin pregnancies whose twinness was the only discrepancy. One discrepant eDiagnosis/medDiagnosis reached no majority (2%). In total, eDiagnoses accuracy was 99.1% (675 + 59 + 38=772 eDiagnoses out of 779 final diagnoses), vs 87.4% (675 + 1 + 5=681) for medDiagnoses accuracy (p &amp;lt; 0.0001). Calculating all basic costs of consultations, medications, surgeries and hospitalisations induced by medDiagnoses versus eDiagnoses, “eDiagEPU” would have saved 3 623.75 € per month. Retrospectively, “eDiaEPU” was usable (99.9%), more accurate for each diagnosis except twinning report and more cost-effective than standard clinical approach. Limitations, reasons for caution The retrospective design is a limitation, as well as the quality of ultrasound interpretation. Some improvements could not derive exclusively from “eDiagEPU” but also from the encoding by a rested or more experienced physician. This software cannot replace clinical and ultrasonographical skills but can improve the diagnostic and therapeutic reasoning. Wider implications of the findings An improved “eDiagEPU” version, considering the diagnosis and management of multiple pregnancies with their specificities (potentially multiple locations, chorioamnionicity) has been developed. Prospective evaluations will be required. Further development steps are considered, including software incorporation into ultrasound devices and integration of previously published predictive/prognostic factors (serum progesterone, corpus luteum scoring...). Trial registration number NCT03993015

Accuracy and Interrater Reliability of Point-of-Care Ultrasonography Image Interpretation for Intussusception.

The aim of this study was to determine the accuracy and interrater reliability of (1) point-of-care ultrasound (POCUS) image interpretation for identification of intussusception and (2) reliability of secondary signs associated with intussusception among experts compared with novice POCUS reviewers. We conducted a planned secondary analysis of a prospective, convenience sample of children aged 3 months to 6 years who were evaluated with POCUS for intussusception across 17 international pediatric emergency departments between October 2018 and December 2020. A random sample of 100 POCUS examinations was reviewed by novice and expert POCUS reviewers. The primary outcome was identification of the presence or absence of intussusception. Secondary outcomes included intussusception size and the presence of trapped free fluid or echogenic foci. Accuracy was summarized using sensitivity and specificity, which were estimated via generalized mixed effects logistic regression. Interrater reliability was summarized via Light's κ statistics with bootstrapped standard errors (SEs). Accuracy and reliability of expert and novice POCUS reviewers were compared. Eighteen expert and 16 novice POCUS reviewers completed the reviews. The average expert sensitivity was 94.5% (95% confidence interval [CI], 88.6-97.5), and the specificity was 94.3% (95% CI, 90.3-96.7), significantly higher than the average novice sensitivity of 84.7% (95% CI, 74.3-91.4) and specificity of 80.4% (95% CI, 72.4, 86.7). κ was significantly greater for expert (0.679, SE 0.039) compared with novice POCUS reviewers (0.424, SE 0.044; difference 0.256, SE 0.033). For our secondary outcome measure of intussusception size, κ was significantly greater for experts (0.661, SE 0.038) compared with novices (0.397, SE 0.041; difference 0.264, SE 0.029). Interrater reliability was weak for expert and minimal for novice reviewers regarding the detection of trapped free fluid and echogenic foci. Expert POCUS reviewers demonstrate high accuracy and moderate interrater reliability when identifying intussusception via image interpretation and perform better than novice reviewers.

Voice-Assisted Image Labeling for Endoscopic Ultrasound Classification Using Neural Networks.

Ultrasound imaging is a commonly used technology for visualising patient anatomy in real-time during diagnostic and therapeutic procedures. High operator dependency and low reproducibility make ultrasound imaging and interpretation challenging with a steep learning curve. Automatic image classification using deep learning has the potential to overcome some of these challenges by supporting ultrasound training in novices, as well as aiding ultrasound image interpretation in patient with complex pathology for more experienced practitioners. However, the use of deep learning methods requires a large amount of data in order to provide accurate results. Labelling large ultrasound datasets is a challenging task because labels are retrospectively assigned to 2D images without the 3D spatial context available in vivo or that would be inferred while visually tracking structures between frames during the procedure. In this work, we propose a multi-modal convolutional neural network (CNN) architecture that labels endoscopic ultrasound (EUS) images from raw verbal comments provided by a clinician during the procedure. We use a CNN composed of two branches, one for voice data and another for image data, which are joined to predict image labels from the spoken names of anatomical landmarks. The network was trained using recorded verbal comments from expert operators. Our results show a prediction accuracy of 76% at image level on a dataset with 5 different labels. We conclude that the addition of spoken commentaries can increase the performance of ultrasound image classification, and eliminate the burden of manually labelling large EUS datasets necessary for deep learning applications.

Reduced training data for robust ultrasound signal interpretation by neural networks

Reduced training data for robust ultrasound signal interpretation by neural networks

Impact of Deliberate Practice on Point-of-Care Ultrasound Interpretation of Right Ventricle Pathology.

BackgroundPoint-of-care (POC) ultrasound (POCUS) has become an essential tool in caring for critically ill patients in several specialties. Mastery in POCUS requires competency in image acquisition, image interpretation, and integration into clinical care. Deliberate practice is an effective method for performance improvement in many areas of medical education; however, it is not well described in the literature for POCUS training.ObjectiveTo analyze differences in the effect of deliberate practice in POCUS image interpretation on performance improvement in groups with varying skill levels.MethodsWe recruited attending physicians and trainees with varying degrees of expertise in POCUS to complete a 50-item educational instrument on the interpretation of right ventricle size and function. The instrument incorporated deliberate practice for the task of correctly identifying right ventricle size and function as either normal or abnormal. Pulmonary critical care trainees obtained and interpreted POCUS images of patients with diagnosed acute pulmonary embolism, which were compared with gold-standard, two-dimensional echocardiographic scans obtained by an expert technician and interpreted by a cardiologist board-certified in echocardiography. We mapped learners’ cumulative accuracy on a learning curve to assess their performance. In addition, we compared groups on the basis of prior experience with using POC echocardiography.ResultsSeventy-nine of 81 participants completed the survey and examination and were included in the analysis. Of the participants, 69 (87.3%) were trainees. The overall cumulative accuracy for the group was 72.9%. All groups demonstrated improvement in accuracy with repetitive practice.ConclusionDeliberate practice in POC echocardiograph interpretation is effective for improving performance in a wide range of learners. Further study is needed to define accuracy cutoffs for competency to help guide learning plans and program requirements and for application into a model for global POC echocardiography competence.

External validation of AIBx, an artificial intelligence model for risk stratification, in thyroid nodules.

BackgroundArtificial intelligence algorithms could be used to risk-stratify thyroid nodules and may reduce the subjectivity of ultrasonography. One such algorithm is AIBx which has shown good performance. However, external validation is crucial prior to clinical implementation.Materials and methodsPatients harboring thyroid nodules 1–4 cm in size, undergoing thyroid surgery from 2014 to 2016 in a single institution, were included. A histological diagnosis was obtained in all cases. Medullary thyroid cancer, metastasis from other cancers, thyroid lymphomas, and purely cystic nodules were excluded. Retrospectively, transverse ultrasound images of the nodules were analyzed by AIBx, and the results were compared with histopathology and Thyroid Imaging Reporting and Data System (TIRADS), calculated by experienced physicians.ResultsOut of 329 patients, 257 nodules from 209 individuals met the eligibility criteria. Fifty-one nodules (20%) were malignant. AIBx had a negative predictive value (NPV) of 89.2%. Sensitivity, specificity, and positive predictive values (PPV) were 78.4, 44.2, and 25.8%, respectively. Considering both TIRADS 4 and TIRADS 5 nodules as malignant lesions resulted in an NPV of 93.0%, while PPV and specificity were only 22.4 and 19.4%, respectively. By combining AIBx with TIRADS, no malignant nodules were overlooked.ConclusionWhen applied to ultrasound images obtained in a different setting than used for training, AIBx had comparable NPVs to TIRADS. AIBx performed even better when combined with TIRADS, thus reducing false negative assessments. These data support the concept of AIBx for thyroid nodules, and this tool may help less experienced operators by reducing the subjectivity inherent to thyroid ultrasound interpretation.

Comparing Time to Diagnosis and Treatment of Patients with Ruptured Ectopic Pregnancy Based on Type of Ultrasound Performed: A Retrospective Inquiry.

Hemorrhage from ruptured ectopic pregnancy is the leading cause of first trimester mortality in North America. Our objective was to compare patients with a ruptured ectopic pregnancy who had an emergency department (ED) point-of-care ultrasound (PoCUS) with those who received a radiology department-performed ultrasound (RADUS). Four time intervals were measured: ED arrival to ultrasound interpretation, obstetric consultation, operating room (OR) arrival, and ultrasound interpretation to OR arrival. This was a retrospective cohort study of patients with ruptured ectopic pregnancy seen between February 2012 and September 2018 at an urban tertiary care hospital. Patients who received an ultrasound in the ED, went directly to the OR, and had confirmed rupture were included. Of 262 patients diagnosed with ectopic pregnancy during the study period, 36 (14%) were ruptured and 32 were enrolled. Comparing times between PoCUS (n=10) and RADUS (n=22) groups, the mean time from ED arrival to ultrasound interpretation was 15 vs. 138 min (difference 123, 95% confidence interval [CI] 88-158), from ED arrival to obstetric consultation was 35 vs. 150 min (difference 115, 95% CI 59-171), from ED arrival to OR arrival was 160 vs. 381 min (difference 222, 95% CI 124-320), and from ultrasound interpretation to OR arrival was 145 vs. 243 min (difference 98, 95% CI 12-184). ED patients with a ruptured ectopic pregnancy who received a PoCUS first had shorter times to diagnosis, obstetric consultation, and OR arrival compared with those who received RADUS.

3-D modeling applications in ultrasound education: a systematic review.

Ultrasound offers a real-time 2-D view of structures within the human body. While many medical education programs have already dedicated a portion of their curriculum to ultrasound, others are concerned about cost, accessibility and limits to student practice. Student benefit may be affected by cognitive errors, which are in part owing to the mental heuristics required to visualize a 3-D structure by interpreting a 2-D image. A possible solution to eliminating subjectivity in ultrasound interpretation is the use of 3-D models to augment the traditional 2-D ultrasound experience. PubMed, Embase and Web of Science were searched for primary literature exploring relationships between 3-D modeling applications and their use in ultrasound education. The search and review process was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. Overall, 14 of the included 16 studies indicated a significant improvement in medical education of ultrasound with the intervention of 3-D modeling applications. This systematic review confirms that 3-D modeling applications benefit student learning in ultrasound education while illuminating the need for more research in this field.

Artificial Intelligence: Innovation to Assist in the Identification of Sono-anatomy for Ultrasound-Guided Regional Anaesthesia.

Ultrasound-guided regional anaesthesia (UGRA) involves the targeted deposition of local anaesthesia to inhibit the function of peripheral nerves. Ultrasound allows the visualisation of nerves and the surrounding structures, to guide needle insertion toa perineural or fascial plane end point for injection. However, it is challenging to develop the necessary skills to acquire and interpret optimal ultrasound images. Sound anatomical knowledge is required and human image analysis is fallible, limited by heuristic behaviours and fatigue, while its subjectivity leads to varied interpretation even amongst experts. Therefore, to maximise the potential benefit of ultrasound guidance, innovation in sono-anatomical identification is required.Artificial intelligence (AI) is rapidly infiltrating many aspects of everyday life. Advances related to medicine have been slower, in part because of the regulatory approval process needing to thoroughly evaluate the risk-benefit ratio of new devices. One area of AI to show significant promise is computer vision (a branch of AI dealing with how computers interpret the visual world), which is particularly relevant to medical image interpretation. AI includes the subfields of machine learning and deep learning, techniques used to interpret or label images. Deep learning systems may hold potential to support ultrasound image interpretation in UGRA but must be trained and validated on data prior to clinical use.Review of the current UGRA literature compares the success and generalisability of deep learning and non-deep learning approaches to image segmentation and explains how computers are able to track structures such as nerves through image frames. We conclude this review with a case study from industry (ScanNav Anatomy Peripheral Nerve Block; Intelligent Ultrasound Limited). This includes a more detailed discussion of the AI approach involved in this system and reviews current evidence of the system performance.The authors discuss how this technology may be best used to assist anaesthetists and what effects this may have on the future of learning and practice of UGRA. Finally, we discuss possible avenues for AI within UGRA and the associated implications.

A National Chinese Survey on Ultrasound Feature Interpretation and Risk Assessment of Breast Masses Under ACR BI-RADS

PurposeThrough this nationwide survey on ACR BI-RADS including ultrasound images of 10 selected breast lesions, we aimed to learn about consistency in feature interpretation and assessment categories and to identify factors that might contribute to inconsistencies, thereby promoting the application of BI-RADS in China.Materials and MethodsThe survey was delivered through a self-developed website about blinded image interpretation and was released to the public through online platforms and social media. A total of 10 representative lesions were selected by an experienced radiologist to gather information about the general practice of BI-RADS lexicons and categories. The Kappa statistic, the chi-squared test, and descriptive statistics were used for data analysis.ResultsNine hundred ultrasound workers completed the questionnaire, coming from all provinces and major cities in China. They had different positions, grades of work organization, and seniority. The interrater agreement of BI-RADS features was fair to substantial (kappa value: 0.37–0.66). For BI-RADS categories, the highest agreement was observed in the typical benign group (average constituent rate = 74.78%), and generally lower agreement was observed in the typical malignant (average constituent rate = 36.03%) and suspicious groups (average constituent rate = 39.02%).ConclusionWe found inconsistencies in BI-RADS applications, providing direction for image feature research using big data. Therefore, we call for more efforts to improve the consistency of BI-RADS application and provide an evidence-based basis for identifying benign and malignant lesions by sonographic features.

Evaluation of the accuracy of ultrasonography compared to histopathological findings in diagnosis of infectious maxillofacial swellings.

Objective: To evaluate the accuracy of ultrasonography in diagnosis of infectious maxillofacial/swelling. Study Design: Simple Cross Sectional study. Setting: Outpatient Department of Oral and Maxillofacial Surgery Unit, Punjab Dental Hospital, Lahore. Period: February 2016 to August 2016. Material &amp; Methods: Conducted on 100 patients. The patients were evaluated with the ultrasound diagnostic modality. The ultrasound interpretation of all the cases was done and best possible diagnosis was made and recorded regarding diagnosis of infectious maxillofacial swellings. The incisional biopsy of every patient was done for histopathology. All findings of ultrasound and histopathology were entered to a proforma. Results: The average age of the patients was 35.81±14.31 years. In this study, 63% patients were males and 37% were females. Out of 100 patients, 70(70%) patients had infectious swelling while 30(30%) did not have infectious swelling. The sensitivity of ultrasonography was calculated as 84.72%, specificity was 67.86%, positive predictive value was 87.14%, negative predictive value was 63.33% and diagnostic accuracy was noted as 80% taking histopathology as gold standard. Conclusion: It was concluded from results of this study that ultrasonography has enough diagnostic accuracy to diagnose infectious swellings of maxillofacial region.