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An ensemble of neural networks provides expert-level prenatal detection of complex congenital heart disease
Congenital heart disease (CHD) is the most common birth defect. Fetal screening ultrasound provides five views of the heart that together can detect 90% of complex CHD, but in practice, sensitivity is as low as 30%. Here, using 107,823 images from 1,326 retrospective echocardiograms and screening ultrasounds from 18- to 24-week fetuses, we trained an ensemble of neural networks to identify recommended cardiac views and distinguish between normal hearts and complex CHD. We also used segmentation models to calculate standard fetal cardiothoracic measurements. In an internal test set of 4,108 fetal surveys (0.9% CHD, >4.4 million images), the model achieved an area under the curve (AUC) of 0.99, 95% sensitivity (95% confidence interval (CI), 84–99%), 96% specificity (95% CI, 95–97%) and 100% negative predictive value in distinguishing normal from abnormal hearts. Model sensitivity was comparable to that of clinicians and remained robust on outside-hospital and lower-quality images. The model’s decisions were based on clinically relevant features. Cardiac measurements correlated with reported measures for normal and abnormal hearts. Applied to guideline-recommended imaging, ensemble learning models could significantly improve detection of fetal CHD, a critical and global diagnostic challenge.
Deep learning can facilitate identification of congenital heart disease from fetal ultrasound screening, a diagnosis that in clinical practice is often missed.
Journal Article
Everyone in their place : the summer of Commissario Ricciardi
Investigating the death of a duchess in 1931 Naples, Commissario Ricciardi is assisted by Brigadier Maione to connect the victim's demise to her associates in the Neapolitan privileged social circles and the local fascist elite.
Book
A wearable cardiac ultrasound imager
Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients
1
–
4
. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness
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11
, and existing wearable cardiac devices can only capture signals on the skin
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16
. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.
Innovations in device design, material fabrication and deep learning are described, leading to a wearable ultrasound transducer capable of dynamic cardiac imaging in various environments and under different conditions.
Journal Article
Blinded, randomized trial of sonographer versus AI cardiac function assessment
Artificial intelligence (AI) has been developed for echocardiography
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3
, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no outside funding) of AI versus sonographer initial assessment of left ventricular ejection fraction (LVEF) to evaluate the impact of AI in the interpretation workflow. The primary end point was the change in the LVEF between initial AI or sonographer assessment and final cardiologist assessment, evaluated by the proportion of studies with substantial change (more than 5% change). From 3,769 echocardiographic studies screened, 274 studies were excluded owing to poor image quality. The proportion of studies substantially changed was 16.8% in the AI group and 27.2% in the sonographer group (difference of −10.4%, 95% confidence interval: −13.2% to −7.7%,
P
< 0.001 for non-inferiority,
P
< 0.001 for superiority). The mean absolute difference between final cardiologist assessment and independent previous cardiologist assessment was 6.29% in the AI group and 7.23% in the sonographer group (difference of −0.96%, 95% confidence interval: −1.34% to −0.54%,
P
< 0.001 for superiority). The AI-guided workflow saved time for both sonographers and cardiologists, and cardiologists were not able to distinguish between the initial assessments by AI versus the sonographer (blinding index of 0.088). For patients undergoing echocardiographic quantification of cardiac function, initial assessment of LVEF by AI was non-inferior to assessment by sonographers.
The impact of artificial intelligence in cardiac function assessment is evaluated by a blinded, randomized non-inferiority trial of artificial intelligence versus sonographer initial assessment of the left ventricular ejection fraction.
Journal Article
Diagnostic accuracy of transthoracic echocardiography for the identification of proximal aortic dissection: a systematic review and meta-analysis
This systematic review and meta-analysis evaluated the performance of transthoracic echocardiography (TTE) for diagnosis of proximal aortic dissections based on the identification of specific sonographic features. A systematic literature search of major databases was conducted on human studies investigating the diagnostic accuracy of TTE for proximal aortic dissection. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The quality of studies was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data were gathered for the following sonographic findings: intimal flap, tear, or intramural hematoma; enlargement of aortic root or widening of aortic walls; aortic valve regurgitation; or pericardial effusion. Sensitivity, specificity, diagnostic odds ratio, number needed to diagnose values, and likelihood ratios were determined. Fourteen studies were included in our final analysis. More than half of the included studies demonstrated low risk of bias. The identification of intimal flap, tear, or intramural hematoma was shown to have an exceptional ability as a diagnostic tool to rule in proximal aortic dissections. TTE should be considered during the initial evaluation of patients presenting to the emergency department with suspected proximal aortic dissection. Positive sonographic findings on TTE may aid in rapid assessment, coordination of care, and treatment of individuals awaiting advanced imaging.
Journal Article
Experiencing Mahler : a listener's companion
\"Arved Ashby takes readers into the seeming chaos of Mahler's work to investigate the elements which make each piece an experiential adventure. The book surveys Mahler's symphonies and song cycles in detail, introducing them as intensely vivid, truthful, and lived and felt experiences\"-- Provided by publisher.
BOOK
A photoacoustic patch for three-dimensional imaging of hemoglobin and core temperature
Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices are unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status than those on the skin surface. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into biological tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a high spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. With access to biomolecules in deep tissues, this technology adds unprecedented capabilities to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice.
The authors present a wearable photoacoustic patch, which integrates laser diodes and piezoelectric transducers for three-dimensional imaging of hemoglobin and temperature in deep tissues.
Journal Article