WATUNet: a deep neural network for segmentation of volumetric sweep imaging ultrasound.

Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks, it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates and attention gates between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Two datasets are utilized for the analysis: the public 'Breast Ultrasound Images' dataset of 780 images and a private VSI dataset of 3818 images, captured at the University of Rochester by the authors. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively. Moreover, our model significantly outperformed other models in McNemar's test with false discovery rate correction on a 381-image VSI set. The experimental findings demonstrate that the proposed WATUNet model achieves precise segmentation of breast lesions in both standard-of-care and VSI images, surpassing state-of-the-art models. Hence, the model holds considerable promise for assisting in lesion identification, an essential step in the clinical diagnosis of breast lesions.

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet.

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

Enhancing Breast Ultrasound Segmentation through Fine-tuning and Optimization Techniques: Sharp Attention UNet.

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it a popular choice among researchers in the medical image segmentation field. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the dice coefficient, specificity, sensitivity, and F1 score obtained values of 0.9283, 0.9936, 0.9426, and 0.9412, respectively. In addition, McNemar's statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperforms the earlier designed models and points towards improved breast lesion segmentation algorithms.

Diagnosis of Pregnancy Complications Using Blind Ultrasound Sweeps Performed by Individuals Without Prior Formal Ultrasound Training.

OBJECTIVE: To estimate the diagnostic accuracy of blind ultrasound sweeps performed with a low-cost, portable ultrasound system by individuals with no prior formal ultrasound training to diagnose common pregnancy complications. METHODS: This is a single-center, prospective cohort study conducted from October 2020 to January 2022 among people with second- and third-trimester pregnancies. Nonspecialists with no prior formal ultrasound training underwent a brief training on a simple eight-step approach to performing a limited obstetric ultrasound examination that uses blind sweeps of a portable ultrasound probe based on external body landmarks. The sweeps were interpreted by five blinded maternal-fetal medicine subspecialists. Sensitivity, specificity, and positive and negative predictive values for blinded ultrasound sweep identification of pregnancy complications (fetal malpresentation, multiple gestations, placenta previa, and abnormal amniotic fluid volume) were compared with a reference standard ultrasonogram as the primary analysis. Kappa for agreement was also assessed. RESULTS: Trainees performed 194 blinded ultrasound examinations on 168 unique pregnant people (248 fetuses) at a mean of 28±5.85 weeks of gestation for a total of 1,552 blinded sweep cine clips. There were 49 ultrasonograms with normal results (control group) and 145 ultrasonograms with abnormal results with known pregnancy complications. In this cohort, the sensitivity for detecting a prespecified pregnancy complication was 91.7% (95% CI 87.2-96.2%) overall, with the highest detection rate for multiple gestations (100%, 95% CI 100-100%) and noncephalic presentation (91.8%, 95% CI 86.4-97.3%). There was high negative predictive value for placenta previa (96.1%, 95% CI 93.5-98.8%) and abnormal amniotic fluid volume (89.5%, 95% CI 85.3-93.6%). There was also substantial to perfect mean agreement for these same outcomes (range 87-99.6% agreement, Cohen κ range 0.59-0.91, P<.001 for all). CONCLUSION: Blind ultrasound sweeps of the gravid abdomen guided by an eight-step protocol using only external anatomic landmarks and performed by previously untrained operators with a low-cost, portable, battery-powered device had excellent sensitivity and specificity for high-risk pregnancy complications such as malpresentation, placenta previa, multiple gestations, and abnormal amniotic fluid volume, similar to results of a diagnostic ultrasound examination using a trained ultrasonographer and standard-of-care ultrasound machine. This approach has the potential to improve access to obstetric ultrasonography globally.

Sustainable volume sweep imaging lung teleultrasound in Peru: Public health perspectives from a new frontier in expanding access to imaging.

Background: Pulmonary disease is a common cause of morbidity and mortality, but the majority of the people in the world lack access to diagnostic imaging for its assessment. We conducted an implementation assessment of a potentially sustainable and cost-effective model for delivery of volume sweep imaging (VSI) lung teleultrasound in Peru. This model allows image acquisition by individuals without prior ultrasound experience after only a few hours of training. Methods: Lung teleultrasound was implemented at 5 sites in rural Peru after a few hours of installation and staff training. Patients were offered free lung VSI teleultrasound examination for concerns of respiratory illness or research purposes. After ultrasound examination, patients were surveyed regarding their experience. Health staff and members of the implementation team also participated in separate interviews detailing their views of the teleultrasound system which were systematically analyzed for key themes. Results: Patients and staff rated their experience with lung teleultrasound as overwhelmingly positive. The lung teleultrasound system was viewed as a potential way to improve access to imaging and the health of rural communities. Detailed interviews with the implementation team revealed obstacles to implementation important for consideration such as gaps in lung ultrasound understanding. Conclusions: Lung VSI teleultrasound was successfully deployed to 5 health centers in rural Peru. Implementation assessment revealed enthusiasm for the system among members of the community along with important areas of consideration for future teleultrasound deployment. This system offers a potential means to increase access to imaging for pulmonary illness and improve the health of the global community.

Assessment of a Brief Standardized Obstetric Ultrasound Training Program for Individuals Without Prior Ultrasound Experience.

ABSTRACT: Obstetric volume sweep imaging (OB VSI) is a simple set of transducer movements guided by external body landmarks that can be taught to ultrasound-naive non-experts. This approach can increase access to ultrasound in rural/low-resources settings lacking trained sonographers. This study presents and evaluates a training program for OB VSI. Six trainees without previous formal ultrasound experience received a training program on the OB VSI protocol containing focused didactics and supervised live hands-on ultrasound scanning practice. Trainees then independently performed 194 OB VSI examinations on pregnancies >14 weeks with known prenatal ultrasound abnormalities. Images were reviewed by maternal-fetal medicine specialists for the primary outcome (protocol deviation rates) and secondary outcomes (examination quality and image quality). Protocol deviation was present in 25.8% of cases, but only 7.7% of these errors affected the diagnostic potential of the ultrasound. Error rate differences between trainees ranged from 8.6% to 53.8% ( P < 0.0001). Image quality was excellent or acceptable in 88.2%, and 96.4% had image quality capable of yielding a diagnostic interpretation. The frequency of protocol deviations decreased over time in the majority of trainees, demonstrating retention of training program over time. This brief OB VSI training program for ultrasound-naive non-experts yielded operators capable of producing high-quality images capable of diagnostic interpretation after 3 hours of training. This training program could be adapted for use by local community members in low-resource/rural settings to increase access to obstetric ultrasound.

Breast Ultrasound Volume Sweep Imaging: A New Horizon in Expanding Imaging Access for Breast Cancer Detection.

OBJECTIVE: The majority of people in the world lack basic access to breast diagnostic imaging resulting in delay to diagnosis of breast cancer. In this study, we tested a volume sweep imaging (VSI) ultrasound protocol for evaluation of palpable breast lumps that can be performed by operators after minimal training without prior ultrasound experience as a means to increase accessibility to breast ultrasound. METHODS: Medical students without prior ultrasound experience were trained for less than 2 hours on the VSI breast ultrasound protocol. Patients presenting with palpable breast lumps for standard of care ultrasound examination were scanned by a trained medical student with the VSI protocol using a Butterfly iQ handheld ultrasound probe. Video clips of the VSI scan imaging were later interpreted by an attending breast imager. Results of VSI scan interpretation were compared to the same-day standard of care ultrasound examination. RESULTS: Medical students scanned 170 palpable lumps with the VSI protocol. There was 97% sensitivity and 100% specificity for a breast mass on VSI corresponding to 97.6% agreement with standard of care (Cohen's κ = 0.95, P < .0001). There was a detection rate of 100% for all cancer presenting as a sonographic mass. High agreement for mass characteristics between VSI and standard of care was observed, including 87% agreement on Breast Imaging-Reporting and Data System assessments (Cohen's κ = 0.82, P < .0001). CONCLUSIONS: Breast ultrasound VSI for palpable lumps offers a promising means to increase access to diagnostic imaging in underserved areas. This approach could decrease delay to diagnosis for breast cancer, potentially improving morbidity and mortality.

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

OBJECTIVES: Pulmonary 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, low-cost, and highly accurate imaging modality for assessment of pulmonary pathology including pneumonia, but its deployment is limited secondary to a lack of trained sonographers. In this study, we piloted a low-cost lung teleultrasound system in rural Peru during the COVID-19 pandemic using lung ultrasound volume sweep imaging (VSI) that can be operated by an individual without prior ultrasound training circumventing many obstacles to ultrasound deployment. DESIGN: Pilot study. SETTING: Study activities took place in five health centres in rural Peru. PARTICIPANTS: There were 213 participants presenting to rural health clinics. INTERVENTIONS: Individuals without prior ultrasound experience in rural Peru underwent brief training on how to use the teleultrasound system and perform lung ultrasound VSI. Subsequently, patients attending clinic were scanned by these previously ultrasound-naïve operators with the teleultrasound system. PRIMARY AND SECONDARY OUTCOME MEASURES: Radiologists examined the ultrasound imaging to assess its diagnostic value and identify any pathology. A random subset of 20% of the scans were analysed for inter-reader reliability. RESULTS: Lung VSI teleultrasound examinations underwent detailed analysis by two cardiothoracic attending radiologists. Of the examinations, 202 were rated of diagnostic image quality (94.8%, 95% CI 90.9% to 97.4%). There was 91% agreement between radiologists on lung ultrasound interpretation among a 20% sample of all examinations (κ=0.76, 95% CI 0.53 to 0.98). Radiologists were able to identify sequelae of COVID-19 with the predominant finding being B-lines. CONCLUSION: Lung VSI teleultrasound performed by individuals without prior training allowed diagnostic imaging of the lungs and identification of sequelae of COVID-19 infection. Deployment of lung VSI teleultrasound holds potential as a low-cost means to improve access to imaging around the world.

Panoramic Reconstruction of B-mode Lung Ultrasound Images Acquired using a Longitudinal Volume Sweep Imaging Protocol.

The ongoing COVID-19 pandemic has already affected more than 300 million people worldwide. Medical imaging shortage affects an estimated of 4 billion people, especially in rural and remote areas (RAs), limiting diagnostic assessment of respiratory illness. Lung ultrasound imaging (LUS) together with volume sweep imaging (VSI) acquisition protocols have been successfully piloted as a solution for lung screening in RAs eliminating the need for trained operators and on-site radiologists. Nevertheless, this protocol requires the acquisition of 12 videos for 6 areas with both longitudinal and transverse positions of the transducer. Nonetheless, bandwidth limitations can hamper the transmission of these videos for remote interpretation. This work aimed to developed a stitching algorithm capable of generating a panoramic reconstruction of LUS cine clips. The results show reconstructions with minimal loss of information as 92.5% of the panoramic images conserved the presence of A-lines. These results show that LUS can be represented as an image without significantly compromising its quality. This can be useful to overcome bandwidth issues as well as improve the time on lung assessment of the patient.

No sonographer, no radiologist: New system for automatic prenatal detection of fetal biometry, fetal presentation, and placental location.

Ultrasound imaging is a vital component of high-quality Obstetric care. In rural and under-resourced communities, the scarcity of ultrasound imaging results in a considerable gap in the healthcare of pregnant mothers. To increase access to ultrasound in these communities, we developed a new automated diagnostic framework operated without an experienced sonographer or interpreting provider for assessment of fetal biometric measurements, fetal presentation, and placental position. This approach involves the use of a standardized volume sweep imaging (VSI) protocol based solely on external body landmarks to obtain imaging without an experienced sonographer and application of a deep learning algorithm (U-Net) for diagnostic assessment without a radiologist. Obstetric VSI ultrasound examinations were performed in Peru by an ultrasound operator with no previous ultrasound experience who underwent 8 hours of training on a standard protocol. The U-Net was trained to automatically segment the fetal head and placental location from the VSI ultrasound acquisitions to subsequently evaluate fetal biometry, fetal presentation, and placental position. In comparison to diagnostic interpretation of VSI acquisitions by a specialist, the U-Net model showed 100% agreement for fetal presentation (Cohen's κ 1 (p<0.0001)) and 76.7% agreement for placental location (Cohen's κ 0.59 (p<0.0001)). This corresponded to 100% sensitivity and specificity for fetal presentation and 87.5% sensitivity and 85.7% specificity for anterior placental location. The method also achieved a low relative error of 5.6% for biparietal diameter and 7.9% for head circumference. Biometry measurements corresponded to estimated gestational age within 2 weeks of those assigned by standard of care examination with up to 89% accuracy. This system could be deployed in rural and underserved areas to provide vital information about a pregnancy without a trained sonographer or interpreting provider. The resulting increased access to ultrasound imaging and diagnosis could improve disparities in healthcare delivery in under-resourced areas.

No sonographer, no radiologist: Assessing accuracy of artificial intelligence on breast ultrasound volume sweep imaging scans.

Breast ultrasound provides a first-line evaluation for breast masses, but the majority of the world lacks access to any form of diagnostic imaging. In this pilot study, we assessed the combination of artificial intelligence (Samsung S-Detect for Breast) with volume sweep imaging (VSI) ultrasound scans to evaluate the possibility of inexpensive, fully automated breast ultrasound acquisition and preliminary interpretation without an experienced sonographer or radiologist. This study was conducted using examinations from a curated data set from a previously published clinical study of breast VSI. Examinations in this data set were obtained by medical students without prior ultrasound experience who performed VSI using a portable Butterfly iQ ultrasound probe. Standard of care ultrasound exams were performed concurrently by an experienced sonographer using a high-end ultrasound machine. Expert-selected VSI images and standard of care images were input into S-Detect which output mass features and classification as "possibly benign" and "possibly malignant." Subsequent comparison of the S-Detect VSI report was made between 1) the standard of care ultrasound report by an expert radiologist, 2) the standard of care ultrasound S-Detect report, 3) the VSI report by an expert radiologist, and 4) the pathological diagnosis. There were 115 masses analyzed by S-Detect from the curated data set. There was substantial agreement of the S-Detect interpretation of VSI among cancers, cysts, fibroadenomas, and lipomas to the expert standard of care ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), the standard of care ultrasound S-Detect interpretation (Cohen's κ = 0.79 (0.65-0.94 95% CI), p<0.0001), the expert VSI ultrasound report (Cohen's κ = 0.73 (0.57-0.9 95% CI), p<0.0001), and the pathological diagnosis (Cohen's κ = 0.80 (0.64-0.95 95% CI), p<0.0001). All pathologically proven cancers (n = 20) were designated as "possibly malignant" by S-Detect with a sensitivity of 100% and specificity of 86%. Integration of artificial intelligence and VSI could allow both acquisition and interpretation of ultrasound images without a sonographer and radiologist. This approach holds potential for increasing access to ultrasound imaging and therefore improving outcomes related to breast cancer in low- and middle- income countries.

Cone-Beam Breast Computed Tomography: Time for a New Paradigm in Breast Imaging.

It is time to reconsider how we image the breast. Although the breast is a 3D structure, we have traditionally used 2D mammography to perform screening and diagnostic imaging. Mammography has been continuously modified and improved, most recently with tomosynthesis and contrast mammography, but it is still using modifications of compression 2D mammography. It is time to consider 3D imaging for this 3D structure. Cone-beam breast computed tomography (CBBCT) is a revolutionary modality that will assist in overcoming the limitations of current imaging for dense breast tissue and overlapping structures. It also allows easy administration of contrast material for functional imaging. With a radiation dose on par with diagnostic mammography, rapid 10 s acquisition, no breast compression, and true high-resolution isotropic imaging, CBBCT has the potential to usher in a new era in breast imaging. These advantages could translate into lower morbidity and mortality from breast cancer.

Lung ultrasound volume sweep imaging for respiratory illness: a new horizon in expanding imaging access.

BACKGROUND: Respiratory illness is a leading cause of morbidity in adults and the number one cause of mortality in children, yet billions of people lack access to medical imaging to assist in its diagnosis. Although ultrasound is highly sensitive and specific for respiratory illness such as pneumonia, its deployment is limited by a lack of sonographers. As a solution, we tested a standardised lung ultrasound volume sweep imaging (VSI) protocol based solely on external body landmarks performed by individuals without prior ultrasound experience after brief training. Each step in the VSI protocol is saved as a video clip for later interpretation by a specialist. METHODS: Dyspneic hospitalised patients were scanned by ultrasound naive operators after 2 hours of training using the lung ultrasound VSI protocol. Separate blinded readers interpreted both lung ultrasound VSI examinations and standard of care chest radiographs to ascertain the diagnostic value of lung VSI considering chest X-ray as the reference standard. Comparison to clinical diagnosis as documented in the medical record and CT (when available) were also performed. Readers offered a final interpretation of normal, abnormal, or indeterminate/borderline for each VSI examination, chest X-ray, and CT. RESULTS: Operators scanned 102 subjects (0-89 years old) for analysis. Lung VSI showed a sensitivity of 93% and a specificity of 91% for an abnormal chest X-ray and a sensitivity of 100% and a specificity of 93% for a clinical diagnosis of pneumonia. When any cases with an indeterminate rating on chest X-ray or ultrasound were excluded (n=38), VSI lung ultrasound showed 92% agreement with chest X-ray (Cohen's κ 0.83 (0.68 to 0.97, p<0.0001)). Among cases with CT (n=21), when any ultrasound with an indeterminate rating was excluded (n=3), there was 100% agreement with VSI. CONCLUSION: Lung VSI performed by previously inexperienced ultrasound operators after brief training showed excellent agreement with chest X-ray and high sensitivity and specificity for a clinical diagnosis of pneumonia. Blinded readers were able to identify other respiratory diseases including pulmonary oedema and pleural effusion. Deployment of lung VSI could benefit the health of the global community.

Testing telediagnostic right upper quadrant abdominal ultrasound in Peru: A new horizon in expanding access to imaging in rural and underserved areas.

BACKGROUND: Hepatic and biliary diseases are prevalent worldwide, but the majority of people lack access to diagnostic medical imaging for their assessment. The liver and gallbladder are readily amenable to sonographic examination, and ultrasound is a portable, cost-effective imaging modality suitable for use in rural and underserved areas. However, the deployment of ultrasound in these settings is limited by the lack of experienced sonographers to perform the exam. In this study, we tested an asynchronous telediagnostic system for right upper quadrant abdominal ultrasound examination operated by individuals without prior ultrasound experience to facilitate deployment of ultrasound to rural and underserved areas. METHODS: The teleultrasound system utilized in this study employs volume sweep imaging and a telemedicine app installed on a tablet which connects to an ultrasound machine. Volume sweep imaging is an ultrasound technique in which an individual scans the target region utilizing preset ultrasound sweeps demarcated by easily recognized external body landmarks. The sweeps are saved as video clips for later interpretation by an experienced radiologist. Teleultrasound scans from a Peruvian clinic obtained by individuals without prior ultrasound experience were sent to the United States for remote interpretation and quality assessment. Standard of care comparison was made to a same-day ultrasound examination performed by a radiologist. RESULTS: Individuals without prior ultrasound experience scanned 144 subjects. Image quality was rated "poor" on 36.8% of exams, "acceptable" on 38.9% of exams, and "excellent" on 24.3% of exams. Among telemedicine exams of "acceptable" or "excellent" image quality (n = 91), greater than 80% of the liver and gallbladder were visualized in the majority of cases. In this group, there was 95% agreement between standard of care and teleultrasound on whether an exam was normal or abnormal, with a Cohen's kappa of 0.84 (95% CI 0.7-0.98, p <0.0001). Finally, among these teleultrasound exams of "acceptable" or "excellent" image quality, the sensitivity for cholelithiasis was 93% (95% CI 68.1%-99.8%), and the specificity was 97% (95% CI 89.5%-99.6%). CONCLUSION: This asynchronous telediagnostic system allows individuals without prior ultrasound experience to effectively scan the liver, gallbladder, and right kidney with a high degree of agreement with standard of care ultrasound. This system can be deployed to improve access to diagnostic imaging in low-resource areas.

Lung Ultrasound: The Essentials.

Although US of the lungs is increasingly used clinically, diagnostic radiologists are not routinely trained in its use and interpretation. Lung US is a highly sensitive and specific modality that aids in the evaluation of the lungs for many different abnormalities, including pneumonia, pleural effusion, pulmonary edema, and pneumothorax. This review provides an overview of lung US to equip the diagnostic radiologist with knowledge needed to interpret this increasingly used modality. Supplemental material is available for this article. © RSNA, 2021.

Testing telediagnostic obstetric ultrasound in Peru: a new horizon in expanding access to prenatal ultrasound.

BACKGROUND: Ninety-four percent of all maternal deaths occur in low- and middle-income countries, and the majority are preventable. Access to quality Obstetric ultrasound can identify some complications leading to maternal and neonatal/perinatal mortality or morbidity and may allow timely referral to higher-resource centers. However, there are significant global inequalities in access to imaging and many challenges to deploying ultrasound to rural areas. In this study, we tested a novel, innovative Obstetric telediagnostic ultrasound system in which the imaging acquisitions are obtained by an operator without prior ultrasound experience using simple scan protocols based only on external body landmarks and uploaded using low-bandwidth internet for asynchronous remote interpretation by an off-site specialist. METHODS: This is a single-center pilot study. A nurse and care technician underwent 8 h of training on the telediagnostic system. Subsequently, 126 patients (68 second trimester and 58 third trimester) were recruited at a health center in Lima, Peru and scanned by these ultrasound-naïve operators. The imaging acquisitions were uploaded by the telemedicine platform and interpreted remotely in the United States. Comparison of telediagnostic imaging was made to a concurrently performed standard of care ultrasound obtained and interpreted by an experienced attending radiologist. Cohen's Kappa was used to test agreement between categorical variables. Intraclass correlation and Bland-Altman plots were used to test agreement between continuous variables. RESULTS: Obstetric ultrasound telediagnosis showed excellent agreement with standard of care ultrasound allowing the identification of number of fetuses (100% agreement), fetal presentation (95.8% agreement, κ =0.78 (p < 0.0001)), placental location (85.6% agreement, κ =0.74 (p < 0.0001)), and assessment of normal/abnormal amniotic fluid volume (99.2% agreement) with sensitivity and specificity > 95% for all variables. Intraclass correlation was good or excellent for all fetal biometric measurements (0.81-0.95). The majority (88.5%) of second trimester ultrasound exam biometry measurements produced dating within 14 days of standard of care ultrasound. CONCLUSION: This Obstetric ultrasound telediagnostic system is a promising means to increase access to diagnostic Obstetric ultrasound in low-resource settings. The telediagnostic system demonstrated excellent agreement with standard of care ultrasound. Fetal biometric measurements were acceptable for use in the detection of gross discrepancies in fetal size requiring further follow up.

New Ultrasound Telediagnostic System for Low-Resource Areas: Pilot Results From Peru.

Billions of people around the world lack access to diagnostic imaging. To address this issue, we piloted a comprehensive ultrasound telediagnostic system, which uses ultrasound volume sweep imaging (VSI) acquisitions capable of being performed by operators without prior traditional ultrasound training and new telemedicine software capable of sending imaging acquisitions asynchronously over low Internet bandwidth for remote interpretation. The telediagnostic system was tested with obstetric, right upper quadrant abdominal, and thyroid volume sweep imaging protocols in Peru. Scans obtained by operators without prior ultrasound experience were sent for remote interpretation by specialists using the telemedicine platform. Scans obtained allowed visualization of the target region in 96% of cases with diagnostic imaging quality. This telediagnostic system shows promise in improving health care disparities in the developing world.

Lung Ultrasound Volume Sweep Imaging for Pneumonia Detection in Rural Areas: Piloting Training in Rural Peru.

OBJECTIVE: Pneumonia is the leading cause of pediatric mortality worldwide among children 0-5 years old. Lung ultrasound can be used to diagnose pneumonia in rural areas as it is a portable and relatively economic imaging modality with ~95% sensitivity and specificity for pneumonia in children. Lack of trained sonographers is the current limiting factor to its deployment in rural areas. In this study, we piloted training of a volume sweep imaging (VSI) ultrasound protocol for pneumonia detection in Peru with rural health workers. VSI may be taught to individuals with limited medical/ultrasound experience as it requires minimal anatomical knowledge and technical skill. In VSI, the target organ is imaged with a series of sweeps and arcs of the ultrasound probe in relation to external body landmarks. METHODS: Rural health workers in Peru were trained on a VSI ultrasound protocol for pneumonia detection. Subjects were given a brief didactic session followed by hands-on practice with the protocol. Each attempt was timed and mistakes were recorded. Participants performed the protocol until they demonstrated two mistake-free attempts. RESULTS: It took participants a median number of three attempts (range 1-6) to perform the VSI protocol correctly. Time to mastery took 51.4 ± 17.7 min. There were no significant differences among doctors, nurses, and technicians in total training time (P = 0.43) or number of attempts to success (P = 0.72). Trainee age was not found to be significantly correlated with training time (P = 0.50) or number of attempts to success (P = 0.40). CONCLUSION: Rural health workers learned a VSI protocol for pneumonia detection with relative ease in a short amount of time. Future studies should investigate the clinical efficacy of this VSI protocol for pneumonia detection. KEY MESSAGE: A volume sweep imaging (VSI) protocol for pneumonia detection can be taught with minimal difficulty to rural health workers without prior ultrasound experience. No difference was found in training performance related to education level or age. VSI involves no significant knowledge of anatomy or technical skill.

Blood finds a way: pictorial review of thoracic collateral vessels.

In the healthy patient, blood returns to the heart via classic venous pathways. Obstruction of any one of these pathways will result in blood flow finding new collateral pathways to return to the heart. Although significant anatomic variation exists and multiple collateral vessels are often present in the same patient, it is a general rule that the collateral pathways formed are a function of the site of venous blockage. Therefore, knowledge of typical collateral vessel systems can provide insight in localizing venous obstruction and characterizing its severity and chronicity. In addition, knowledge of collateral anatomy can be essential in interventional procedural and/or surgical planning, especially when placing catheters in patients with venous blockage. In this pictorial review, we provide a systematic approach to understanding collateral pathways in patients with venous obstruction in the upper body.

Pictorial review of the pulmonary vasculature: from arteries to veins.

Pathology of the pulmonary vasculature involves an impressive array of both congenital and acquired conditions. While some of these disorders are benign, disruption of the pulmonary vasculature is often incompatible with life, making these conditions critical to identify on imaging. Many reviews of pulmonary vascular pathology approach the pulmonary arteries, pulmonary veins and bronchial arteries as individual topics. The goal of this review is to provide an integrated overview of the high-yield features of all major disorders of the pulmonary vasculature. This approach provides a more cohesive and comprehensive conceptualisation of respiratory pathology. In this review, we present both the salient clinical and imaging features of congenital and acquired disorders of the pulmonary vasculature, to assist the radiologist in identifying pathology and forming a robust differential diagnosis tailored to the presenting patient. TEACHING POINTS: • Abnormalities of the pulmonary vasculature are both congenital and acquired. • Pathology of a single pulmonary vascular territory often affects the entire pulmonary vasculature. • Anomalous pulmonary venous flow is named as a function of its location and severity. • Bronchial arteries often undergo dilatation secondary to cardio-respiratory pathology.