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

Do Ultrasound Lung Abnormalities Correlate to Biomarkers and Male Gender in Rheumatoid Arthritis Patients? A Monocentric Cross-Sectional Study

Background: Lung ultrasound (LUS) is a tool of growing interest in Rheumatoid Arthritis (RA) oligo- symptomatic ILD to avoid. Objective: We aimed to evaluate (i) the prevalence of pleural (PLUS) and parenchymal (PAUS) abnormalities in LUS in the RA population and their possible correlation to biomarkers; (ii) the predictivity of gender, smoking habits, previous infections (past COVID-19 tuberculosis), and treatments; (iii) the differences in LUS between sexes. Methods: We collected the data of 155 (15 early and 140 late) RA patients with mild respiratory symptoms, evaluating PLUS and PAUS, in fourteen lung areas and also summing the scores (LUS-T). Results: Only 13/155 (8.4%) were completely negative; LUS correlated to age (all parameters p 0.0001), rheumatoid factor IgM (PLUS p 0.0006, PAUS p 0.02, LUS-T p 0.001) and ACPA (p 0.001, 0.006, 0.001, respectively), and PLUS also correlated to IL6 (p 0.02). The male gender was predictive of all LUS evaluations (p 0.001, 0.05, 0.001, respectively), which were higher than in women (p 0.001, 0.01, 0.001, respectively). Other potential risk factors were independent, except biological treatments, which showed a low predictivity to PLUS (p < 0.05). Conclusions: We can conclude that LUS is a useful technique in RA low respiratory symptoms and correlates with age, the most important RA biomarkers, and male sex.

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.

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.

Enablers and barriers to adopt the locally developed Masi mechanical ventilator amid COVID-19 pandemic in Peru

Background

Limited supply of resources during the COVID-19 emergency encouraged the local development of the Masi mechanical ventilator (MV). Despite the efforts to promote Masi, adopting this innovation faced multiple obstacles, regardless of its performance. We explored the perceptions among healthcare personnel towards incorporating Masi to provide ventilatory support to COVID-19 patients during the second wave in Peru (January to June 2021).

Methods

We conducted twelve in-depth virtual interviews. Topics included experience when handling Masi, the impact of the training received, confidence in the device, barriers perceived, and enablers identified. All participants provided verbal informed consent.

Results

Most of the participants were male physicians. Participants belonged to seven hospitals that exhibited a wide range of healthcare capacities. Globally, the adoption of Masi MV was driven by the scarcity of ventilatory devices in the wards and reinforced by appropriate training and prompt technical support. Participants reported that Masi's structural and operational features played both advantages and disadvantages. Hospital infrastructure readiness, availability of commercial MVs, mistrust in its simple appearance, and resistance to change among healthcare personnel were perceived as barriers, while low-cost, prompt technical support and user-friendliness were valuable enablers. The first two enablers were observed in participants regardless of their attitude towards Masi. Despite the small number of participants for this qualitative study, it is important to note that the sample size was sufficient to reach saturation, as the topics discussed with participants became redundant and did not yield new information.

Conclusions

The perceptions among healthcare personnel to incorporate Masi as a mechanical ventilator for COVID-19 patients showed that communication, training and experience, and peer encouragement were essential to secure its use and sustainability of the technology. A priori judgments and perceptions unrelated to the performance of the novel device were observed, and its proper management may define its further implementation. Altogether our study suggests that along with strengthening local technological development, strategies to improve their adoption process must be considered as early as possible in medical innovations.

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.

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.

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.