Automated interpretation of systolic and diastolic function on the echocardiogram:a multicohort study

Background: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic function to diagnose and manage heart failure. However, manual interpretation of echocardiograms can be time consuming and subject to human error. Therefore, we developed a fully automated deep learning workflow to classify, segment, and annotate two-dimensional (2D) videos and Doppler modalities in echocardiograms. Methods: We developed the workflow using a training dataset of 1145 echocardiograms and an internal test set of 406 echocardiograms from the prospective heart failure research platform (Asian Network for Translational Research and Cardiovascular Trials; ATTRaCT) in Asia, with previous manual tracings by expert sonographers. We validated the workflow against manual measurements in a curated dataset from Canada (Alberta Heart Failure Etiology and Analysis Research Team; HEART; n=1029 echocardiograms), a real-world dataset from Taiwan (n=31 241), the US-based EchoNet-Dynamic dataset (n=10 030), and in an independent prospective assessment of the Asian (ATTRaCT) and Canadian (Alberta HEART) datasets (n=142) with repeated independent measurements by two expert sonographers. Findings: In the ATTRaCT test set, the automated workflow classified 2D videos and Doppler modalities with accuracies (number of correct predictions divided by the total number of predictions) ranging from 0·91 to 0·99. Segmentations of the left ventricle and left atrium were accurate, with a mean Dice similarity coefficient greater than 93% for all. In the external datasets (n=1029 to 10 030 echocardiograms used as input), automated measurements showed good agreement with locally measured values, with a mean absolute error range of 9–25 mL for left ventricular volumes, 6–10% for left ventricular ejection fraction (LVEF), and 1·8–2·2 for the ratio of the mitral inflow E wave to the tissue Doppler e' wave (E/e' ratio); and reliably classified systolic dysfunction (LVEF <40%, area under the receiver operating characteristic curve [AUC] range 0·90–0·92) and diastolic dysfunction (E/e' ratio ≥13, AUC range 0·91–0·91), with narrow 95% CIs for AUC values. Independent prospective evaluation confirmed less variance of automated compared with human expert measurements, with all individual equivalence coefficients being less than 0 for all measurements. Interpretation: Deep learning algorithms can automatically annotate 2D videos and Doppler modalities with similar accuracy to manual measurements by expert sonographers. Use of an automated workflow might accelerate access, improve quality, and reduce costs in diagnosing and managing heart failure globally. Funding: A*STAR Biomedical Research Council and A*STAR Exploit Technologies

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.)

Cascade of classifiers to classify interictal EEGs of patients with epilepsy

Epilepsy is a chronic disease influencing many people’s health worldwide. According to the study of the WHO, there are over 50 million epilepsy patients around the world. Now, electroencephalogram (EEG) is still a primary method to analyze epilepsy. Experts can detect epilepsy by visual analysis of EEGs, which record the electrical signals of the human brain. Epileptiform transients (ET) or spikes usually appear in the EEG of epileptic patients. The spikes are the main indicators for epilepsy. However, detecting epilepsy by only visual inspection may need couple of hours, and there is a lack of experts who can read EEGs. Moreover, there is no standard definition for spikes, which makes the spike detection based diagnosis of epilepsy, tedious and expert-centered. Experts do not always agree on which waveforms are spikes and which ones are not. Hence, an automated method for analysis of epileptic patients’ EEG data is of importance for management and diagnosis of epilepsy. Many methods have been applied to detect the spikes such as template matching, neural network, SVM or random forest. In this thesis, we develop an efficient classification method to eliminate most background waveforms through an effective cascade of classifiers. A cascade of winning classifiers is designed to reject most background waveform for EEG data in several consecutive stages, while prereserving most spikes. Validating a classification method needs sufficiently large data. We have used 93 epileptic patients’ EEG data from Massachusetts General Hospital, which include 18164 spikes in total. We apply the 10-step cascade of decision tree, random forest and (support vector machine) SVM separately to the data by applying cross validation. In the numerical tests of this study, on average, the cascade of decision tree rejected 98.94%% of all background in the EEG dataset while preserving 86.22% of the spikes. The cascade of SVM rejected 98.89% of all background in the EEG dataset while preserving 86.97% of the spikes. The cascade of random forest rejected 98.84% of all background in the EEG dataset while preserving 87.32% of the spikes.Master of Science (Computer Control and Automation

An Experimental Investigation on the Lubrication Performance of Water-Based Nanolubricant With TiO2 as Nanoadditive

In this work, an experimental investigation on the lubrication performance of water-based nanolubricant with TiO2 as nanoadditives was performed. The nanolubricants show excellent dispersibility and stability within 5 days after preparation. Additionally, the results show that the concentration of TiO2 nanoparticles significantly affects the lubrication performance of the prepared nanolubricant during microrolling. The excessive nanoparticles lead to agglomeration at the contact area, while the limited nanoparticles can hardly work during rolling processes. Instead, for a nanolubricant with 3.0 wt% of TiO2 nanoparticles, the nanolubricant is able to feed abundant nanoparticles continuously to the rubbing zone with little agglomeration, improving the surface quality of copper foils during microrolling

A study on the forming of microchannels by micro rolling of copper foils

Copper microchannels have been attracting more and more attention due to the increasing demands for multifunctional microcomponents in the field of micromanufacturing. In the present work, the forming of microchannels on copper foils was studied by micro rolling. Copper foils with the thickness of 0.1 mm were selected and annealed at 400, 500, 600, 700 and 800 °C for 10 min prior to micro rolling, and the formability and quality of microchannels were systematically investigated. The results show that an optimal annealing temperature of 500 °C is beneficial to the forming of microchannels with high surface quality. A series of electron backscatter diffraction (EBSD) tests were performed in order to explore the effect of annealing temperatures on the formability of copper foils during micro rolling, and the results indicate that the high forming accuracy of microchannels with copper foils annealed at 500 °C is mainly attributed to the high geometric dislocation density. Additionally, the weakening of major texture components (the Brass, S and Goss components) through optimization of heat treatment also contributes to the improvement of forming accuracy of microchannels, promoting the forming of high-quality microchannels by micro rolling process

Human vs. Machine: The Brain and Optic Nerve Study with Artificial Intelligence (BONSAI) (Slides)

We developed and validated an artificial intelligence deep learning system (AI-DLS) to automatically classify optic discs as "normal" or "abnormal", and specifically detect "papilledema", but direct comparison of the diagnostic accuracy of a DLS versus expert neuro-ophthalmologists using the same sample is warranted. Our objective was to compare the diagnostic performance of an AI-DLS versus expert neuro-o phthalmologists in classifying optic nerves as "normal", "papilledema" (optic nerve edema from proven intracranial hypertension), and "other optic nerve abnormalities" on ocular fundus photographs

Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography

BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population aged 75 years and above 1. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment. METHODS: 2-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai, Singapore) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed. RESULTS: Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, p < 0.001), mean pressure gradient (r = 0.94, p < 0.001), aortic valve area by continuity equation (r = 0.88, p < 0.001), stroke volume index (r = 0.79, p < 0.001), left ventricular outflow tract velocity time integral (r = 0.89, p < 0.001), aortic valve velocity time integral (r = 0.96, p < 0.001), and left ventricular outflow tract diameter (r = 0.76, p < 0.001). CONCLUSIONS: Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize inter-scan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with aortic stenosis

Artificial Intelligence for Detection and Classification of Abnormal Optic Discs on Fundus Photographs

The aim of this study was to assess the performance of artificial intelligence, using a deep learning algorithm, for automated detection and classification of abnormal optic nerve heads, based on colour fundus photographs