Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition. The approach presented here is based on the analysis of brightness distribution patterns present in rectangular segments (here called “characteristic vectors“) from the ultrasound digital images. In a first step we identified and eliminated the skin and subcutaneous tissue (fat and muscle) in lung ultrasound frames, and the “characteristic vectors”were analyzed using standard neural networks using artificial intelligence methods. We analyzed 60 lung ultrasound frames corresponding to 21 children under age 5 years (15 children with confirmed pneumonia by clinical examination and X-rays, and 6 children with no pulmonary disease) from a hospital based population in Lima, Peru. Lung ultrasound images were obtained using an Ultrasonix ultrasound device. A total of 1450 positive (pneumonia) and 1605 negative (normal lung) vectors were analyzed with standard neural networks, and used to create an algorithm to differentiate lung infiltrates from healthy lung. A neural network was trained using the algorithm and it was able to correctly identify pneumonia infiltrates, with 90.9% sensitivity and 100% specificity. This approach may be used to develop operator-independent computer algorithms for pneumonia diagnosis using ultrasound in young children.

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.

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.

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.

BackgroundRespiratory 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.MethodsDyspneic 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.ResultsOperators 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.ConclusionLung 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.