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

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.

Feasibility and Safety of 5G-Based Telerobotic Abdominal Ultrasonography

OBJECTIVE

Telemedicine can offer services to remote patients regardless of the distance. Fifth-generation (5G) mobile networks may make telemedicine practical because of their low latency. This study aimed to evaluate the feasibility and safety of a novel 5G robot-assisted remote abdominal ultrasound (AUS) telemedicine technology in clinical applications in distant locations.

METHODS

We performed 5G-based telerobotic AUS in patients who were located more than 100 km away from the physicians.

RESULTS

The telerobotic AUS had a longer examination time than the conditional bedside AUS; however, the complete examination rate was not inferior. None of the volunteers experienced discomfort during the examination and the examination time was acceptable for all.

CONCLUSION

Our findings confirm the feasibility and safety of 5G-based telerobotic AUS in clinical practice.

Baran T, O’Connell A, Parker K. 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.

Paramedic-Performed Prehospital Tele-Ultrasound: A Powerful Technology or an Impractical Endeavor? A Scoping Review

Ultrasound with remote assistance (tele-ultrasound) may have potential to improve accessibility of ultrasound for prehospital patients. A review of recent literature on this topic has not been done before, and the feasibility of prehospital tele-ultrasound performed by non-physician personnel is unclear. In an effort to address this, the literature was qualitatively analyzed from January 1, 2010 - December 31, 2021 in the MEDLINE, EMBASE, and Cochrane online databases on prehospital, paramedic-acquired tele-ultrasound, and ten articles were found. There was considerable heterogeneity in the study design, technologies used, and the amount of ultrasound training for the paramedics, preventing cross-comparisons of different studies. Tele-ultrasound has potential to improve ultrasound accessibility by leveraging skills of a remote ultrasound expert, but there are still technological barriers to overcome before determinations on feasibility can be made.

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.

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.

Integrating Telehealth for Strengthening Health Systems in the Context of the COVID-19 Pandemic: A Perspective from Peru

The COVID-19 pandemic forced the government to rapidly modify its legal framework to adopt telemedicine and promote the implementation of telehealth services to meet the healthcare needs of patients in Peru. In this paper, we aim to review the main changes to the regulatory framework and describe selected initiatives to promote the telehealth framework that emerged in Peru during the COVID-19 pandemic. In addition, we discuss the challenges to integrate telehealth services for strengthening health systems in Peru. The Peruvian telehealth regulatory framework began in 2005, and in subsequent years, laws and regulations were established that sought to progressively implement a national telehealth network. However, mainly local initiatives were deployed. In this sense, significant challenges remain to be addressed, such as infrastructure in healthcare centers, including high-speed Internet connectivity; infostructure of health-information systems, including interoperability with electronic medical records; monitoring and evaluation of the national agenda for the health sector in 2020-2025; expanding the healthcare workforce in terms of digital health; and developing the capacities of healthcare users on health literacy, including digital aspects. In addition, there is enormous potential for telemedicine as a key strategy to deal with the COVID-19 pandemic and to improve access to rural and hard-to-reach areas and populations. There is thus an urgent need to effectively implement an integrated national telehealth system to address sociocultural issues and strengthen the competencies of human resources in telehealth and digital health in Peru.

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.

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.

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.

Reliability of 3-D Virtual Abdominal Tele-ultrasonography in Pediatric Emergency: Comparison with Standard-of-Care Ultrasound Examination

Ultrasound is currently recommended as the first-line examination for abdominal symptoms in children. However, a pediatric radiologist is not always available on site, especially during on-call duty. This study was aimed at evaluating the reliability of an innovative 3-D virtual abdominal tele-ultrasonography in this context. A prospective study was conducted between December 2020 and May 2021 that recruited 103 children undergoing ultrasound for abdominal pain. Trauma cases were excluded. Four tridimensional acquisitions were performed with a Smart Sensor 3D device (Canon Medical Systems, Otawara, Japan). Each tele-ultrasonography was secondarily blindly reviewed by two radiologists (one senior and one resident) with Fusion software (Canon Medical Systems). Acceptance and quality of the acquisitions were evaluated on a Likert scale. Inter-rater reliability was quantified using Cohen's κ coefficient and intraclass correlation coefficient. The ultrasound examination was normal in 66 cases (64%), abnormal in 36 cases (35%) and inconclusive in 1 case (1%). The acquisitions were obtained without objections from the children, their parents or the operators in more than 95% of cases. The quality of the acquisitions was considered good to excellent in 84% and 70% of cases. The sensitivity of the senior radiologist and the resident was 86% and 84%, respectively; specificity was 95% and 92%, positive predictive value 92% and 86% and negative predictive value 92 and 91% when comparing the conclusions of the standard and the tele-ultrasound examinations. Cohen's κ coefficients of the diagnosis obtained with the standard and the tele-ultrasound examinations were 0.82 and 0.71, respectively. The inter-rater Cohen's κ coefficient was 0.84. The intraclass correlation coefficient between the standard abdominal examination and the 3-D tele-ultrasound reformatted images for the following quantitative variables on pathological cases was 0.99 (confidence interval: 0.98-0.99). Virtual abdominal tele-ultrasonography is a promising method in pediatric emergencies.

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.

Successful Use of a 5G-Based Robot-Assisted Remote Ultrasound System in a Care Center for Disabled Patients in Rural China

Background

Disability has become a global population health challenge. Due to difficulties in self-care or independent living, patients with disability mainly live in community-based care centers or institutions for long-term care. Nonetheless, these settings often lack basic medical resources, such as ultrasonography. Thus, remote ultrasonic robot technology for clinical applications across wide regions is imperative. To date, few experiences of remote diagnostic systems in rural care centers have been reported.

Objective

To assess the feasibility of a fifth-generation cellular technology (5G)-based robot-assisted remote ultrasound system in a care center for disabled patients in rural China.

Methods

Patients underwent remote robot-assisted and bedside ultrasound examinations of the liver, gallbladder, spleen, and kidneys. We compared the diagnostic consistency and differences between the two modalities and evaluated the examination duration, image quality, and safety.

Results

Forty-nine patients were included (21 men; mean age: 61.0 ± 19.0 [range: 19-91] years). Thirty-nine and ten had positive and negative results, respectively; 67 lesions were detected. Comparing the methods, 41 and 8 patients had consistent and inconsistent diagnoses, respectively. The McNemar and kappa values were 0.727 and 0.601, respectively. The mean duration of remote and bedside examinations was 12.2 ± 4.5 (range: 5-26) min and 7.5 ± 1.8 (range: 5-13) min (p < 0.001), respectively. The median image score for original images on the patient side and transmitted images on the doctor side was 5 points (interquartile range: [IQR]: 4.7-5.0) and 4.7 points (IQR: 4.5-5.0) (p = 0.176), respectively. No obvious complications from the examination were reported.

Conclusions

A 5G-based robot-assisted remote ultrasound system is feasible and has comparable diagnostic efficiency to traditional bedside ultrasound. This system may provide a unique solution for basic ultrasound diagnostic services in primary healthcare settings.

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.

Tele-ultrasound in the Era of COVID-19: A Practical Guide

Telemedicine has evolved over the past 50 years, with video consultations and telehealth (TH) mobile apps that are now widely used to support care in the management of chronic conditions, but are infrequently used in acute conditions such as emergencies. In the wake of the COVID-19 pandemic, demand is growing for video consultations as they minimize health provider-patient interactions and thereby the risk of infection. Advanced applications such as tele-ultrasound (TUS) have not yet gained a foothold despite their achieving technical maturity and the availability of software from numerous companies for TUS for their respective portable ultrasound devices. However, ultrasound is indispensable for triage in emergencies and also offers distinct advantages in the diagnosis of COVID-19 pneumonia for certain patient populations such as pregnant women, children and immobilized patients. Additionally, recent work suggests lung ultrasound can accurately risk stratify patients for likely infection when immediate polymerase chain reaction (PCR) testing is not available and has prognostic utility for positive patients with respect to the need for admission and intensive care unit (ICU) treatment. Though currently underutilized, a wider implementation of TUS in TH applications and processes may be an important stepping-stone for telemedicine. The addition of ultrasound to TH may allow it to cross the barrier from being an application used mainly for primary care and chronic conditions to an indispensable tool used in emergency care, disaster situations, remote areas and low-income countries where it is difficult to obtain high-quality diagnostic imaging. The objective of this review was to provide an overview of the current state of telemedicine, insights into current and future use scenarios, its practical application as well as current TUS uses and their potential value with an overview of currently available portable and handheld ultrasound devices. In the wake of the COVID-19 pandemic we point out an unmet need and use case of TUS as a supportive tool for health care providers and organizations in the management of affected patients.

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.

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.