Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

Abstract: A computer-implemented method for grading of Aortic Stenosis severity performed by an automated workflow engine executed by at least one processor includes receiving, from a memory, a plurality of echocardiogram images a heart. The plurality of echocardiogram (echo) images according to 2D images and Doppler modality images. The 2D images are classified by view type, and the Doppler modality images are classified by region. The regions of interest in the 2D images are segmented to produce segmented 2D images. The Doppler modality images are segmented to generate waveform traces to produce segmented Doppler modality images. The segmented images are used to calculate measurements of cardiac features of the heart. A conclusion of Aortic Stenosis severity is generated by comparing the calculated measurements to cardiac guidelines. A report is then output showing the calculated measurements of the cardiac features that fall within or outside of the cardiac guidelines.

Abstract: A computer-implemented method for automated diagnosis of cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) performed by an automated workflow engine executed by at least one processor includes separating a plurality of echocardiogram (echo) images a heart according to 2D images and Doppler modality images. The 2D images are classified by view type, including A4C video. The 2D images are segmented to produce segmented A4C images having a segmentation mask over the left ventricle. Phase detection is performed on the segmented A4C images to determine systole and diastole endpoints per cardiac cycle. Disease classification is performed on beat-to-beat A4C images for respective cardiac cycles. The cardiac cycle probability scores generated for all of the cardiac cycles are aggregated for each A4C video, and the aggregated probability scores for all the A4C videos are combined to generate a patient-level conclusion for CA and HCM.

Abstract: An automated workflow receives a patient study comprising cardiac biomarker measurements and a plurality of echocardiographic images taken by an ultrasound device of a patient heart. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The cardiac biomarker measurements and the calculated measurements are compared with international cardiac guidelines to generate conclusions, and a report is output showing the measurements that fall within or outside of the guidelines.

Abstract: A method for training neural networks of an automated workflow performed by a software component executing on a server in communication with remote computers at respective laboratories includes downloading and installing a client and a set of neural networks to a first remote computer of a first laboratory, the client accessing the echocardiogram image files of the first laboratory to train the set of neural networks and to upload a first trained set of neural networks to the server. The process continues until the client and the second trained set of neural networks is downloaded and installed to a last remote computer of a last laboratory, the client accessing the echocardiogram image files of the last laboratory to continue to train the second trained set of neural networks and to upload a final trained set of neural networks to the server.

Abstract: Artificial intelligence (AI) recognition of echocardiogram (echo) images by a mobile ultrasound device comprises receiving a plurality of the echo images captured by the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user, the echo images comprising 2D images and Doppler modality images of a heart. One or more neural networks process the echo images to automatically classify the echo images by view type. The view type of the echo images is simultaneously displayed in the UI of the ultrasound device along with the echo images. A report is generated showing the calculated measurements of features in the echo images. The report showing the calculated measurements is displayed on a display device.

Abstract: Artificial intelligence (AI)-based guidance for an ultrasound device to improve capture of echo image views comprises receiving a plurality of the echo images captured by a probe of the ultrasound device, the ultrasound device including a display and a user interface (UI) that displays the echo images to a user. One or more neural networks process the echo images to continuously attempt to automatically classify the echo images by view type and generates corresponding classification confidence scores. The view type of the echo images are simultaneously displayed in the UI along with the echo images. Feedback indications are displayed in the UI of the ultrasound device to the user, where the feedback indications include which directions to move the probe of the ultrasound device so the probe can be placed in a correct position to capture and successfully classify the echo images.

Abstract: An automated workflow receives a patient study comprising cardiac biomarker measurements and a plurality of echocardiographic images taken by an ultrasound device of a patient heart. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The cardiac biomarker measurements and the calculated measurements are compared with international cardiac guidelines to generate conclusions, and a report is output showing the measurements that fall within or outside of the guidelines.

Abstract: A method for training neural networks of an automated workflow performed by a software component executing on a server in communication with remote computers at respective laboratories includes downloading and installing a client and a set of neural networks to a first remote computer of a first laboratory, the client accessing the echocardiogram image files of the first laboratory to train the set of neural networks and to upload a first trained set of neural networks to the server. The process continues until the client and the second trained set of neural networks is downloaded and installed to a last remote computer of a last laboratory, the client accessing the echocardiogram image files of the last laboratory to continue to train the second trained set of neural networks and to upload a final trained set of neural networks to the server.

Abstract: An automated workflow performed by software executing on at least one processor includes receiving a plurality of echocardiogram images taken by an ultrasound device. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The calculated measurements are compared with international cardiac guidelines to generate conclusions and a report is output showing the calculated measurements that fall both within and outside of the guidelines.

Abstract: An automated workflow performed by software executing on at least one processor includes receiving a plurality of echocardiogram images taken by an ultrasound device. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The calculated measurements are compared with international cardiac guidelines to generate conclusions and a report is output showing the calculated measurements that fall both within and outside of the guidelines.

Abstract: An automated workflow performed by software executing on at least one processor includes receiving a plurality of echocardiogram images taken by an ultrasound device. A first filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. A first neural network classifies the 2D images by view type, and a second neural network classifies the Doppler modality images by view type. A third neural network segments cardiac chambers in the 2D images and a fourth neural network segments the Doppler modality images to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The calculated measurements are compared with international cardiac guidelines to generate conclusions and a report is output highlighting the measurements that fall outside of the guidelines.

Abstract: In exemplary implementations of this invention, stimuli are intermittently presented to the left or right side of a user. For example, this invention may comprise a method of presenting stimuli to a bilateral organism, which organism has a left side and a right side, wherein: (a) the stimuli are produced by at least one transducer, (b) the stimuli include an intermittent beat train, and (c) the beat train has a beat frequency that is substantially equal to 7.8×(1.618)n Hz, where n is an even integer, which even integer may be negative, zero or positive.

Abstract: In exemplary implementations of this invention, stimuli are intermittently presented to the left or right side of a user. For example, this invention may comprise a method of presenting stimuli to a bilateral organism, which organism has a left side and a right side, wherein: (a) the stimuli are produced by at least one transducer, (b) the stimuli include an intermittent beat train, and (c) the beat train has a beat frequency that is substantially equal to 7.8×(1.618)n Hz, where n is an even integer, which even integer may be negative, zero or positive.

Abstract: In exemplary implementations of this invention, stimuli are intermittently presented to the left or right side of a user. For example, this invention may comprise a method of presenting stimuli to a bilateral organism, which organism has a left side and a right side, wherein: (a) the stimuli are produced by at least one transducer, (b) the stimuli include an intermittent beat train, and (c) the beat train has a beat frequency that is substantially equal to 7.8×(1.618)n Hz, where n is an even integer, which even integer may be negative, zero or positive.

Abstract: A method for removing a tubular from a wellbore can include repeatedly and sequentially using an upper gripping member of an upper section and a lower gripping member of a lower section to hydraulically grip the tubular, while repeatedly and sequentially extending and retracting the upper section relative to the lower section to raise the tubular from the wellbore to a predetermined height. At least one lifting hole can be formed in the tubular, and a lifting member can be installed in each lifting hole. A hoist can be connected with each lifting member. The tubular can be cut using a saw and removed from the wellbore using the hoist.

Abstract: A tubular lift safety system can include a wellbore tubular removal system for removing tubulars from wellbores, which can have an upper section movable relative to a lower section. The upper section and the lower section can both have gripping members for gripping and releasing the tubulars while the upper section is moved relative to the lower section; thereby removing the tubulars from the wellbore. The upper section can have cutting devices for forming lifting holes and a saw for cutting the tubulars. A fluid source can control extension and retraction of the upper section relative to the lower section, operation of the gripping members, and operation of the saw. A remote control can monitor pressure from the fluid source.

Abstract: A wellbore tubular lift safety system and tubular lifting apparatus for removing tubulars can include a lower section with a lower base plate hole, sliding rods, and a lower gripping member. An upper section can have an upper base plate hole, a saw mounting plate hole, an upper gripping member, and cylinder barrels. The sliding rods can be engaged within the cylinder barrels, and can be moved therein for extending and retracting the upper section. The apparatus can be positioned over the wellbore in a retracted position to align with the tubular. The tubular can be lifted out of the wellbore by gripping the tubular using the gripping members, and extending and retracting the upper section. The apparatus can form lifting holes in the tubular, install lifting members in the lifting holes, saw the tubular, and allow the cut tubular to be lifted via a hoist.

Abstract: An orthopaedic instrument includes a metallic load-bearing member and a non-metallic support structure formed integrally with the load-bearing member such that the load-bearing member is permanently attached to the non-metallic support structure. The non-metallic support structure enables the orthopaedic instrument to be lighter than an all-metal instrument while the metallic load bearing member provides wearability comparable to an all-metal instrument.