2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a UK-Wide Collaborative.

BACKGROUND: Global longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake. OBJECTIVES: The authors developed open machine-learning-based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative. METHODS: We trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages. RESULTS: The median GLS, averaged across the 11 individual experts, was -16.1 (IQR: -19.3 to -12.5). Using each case's expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74. CONCLUSIONS: Our open-source approach to calculating GLS agrees with experts' consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online.

Automated echocardiographic left ventricular dimension assessment in dogs using artificial intelligence: Development and validation.

BACKGROUND: Artificial intelligence (AI) could improve accuracy and reproducibility of echocardiographic measurements in dogs. HYPOTHESIS: A neural network can be trained to measure echocardiographic left ventricular (LV) linear dimensions in dogs. ANIMALS: Training dataset: 1398 frames from 461 canine echocardiograms from a single specialist center. VALIDATION: 50 additional echocardiograms from the same center. METHODS: Training dataset: a right parasternal 4-chamber long axis frame from each study, labeled by 1 of 18 echocardiographers, marking anterior and posterior points of the septum and free wall. VALIDATION DATASET: End-diastolic and end-systolic frames from 50 studies, annotated twice (blindly) by 13 experts, producing 26 measurements of each site from each frame. The neural network also made these measurements. We quantified its accuracy as the deviation from the expert consensus, using the individual-expert deviation from consensus as context for acceptable variation. The deviation of the AI measurement away from the expert consensus was assessed on each individual frame and compared with the root-mean-square-variation of the individual expert opinions away from that consensus. RESULTS: For the septum in end-diastole, individual expert opinions deviated by 0.12 cm from the consensus, while the AI deviated by 0.11 cm (P = .61). For LVD, the corresponding values were 0.20 cm for experts and 0.13 cm for AI (P = .65); for the free wall, experts 0.20 cm, AI 0.13 cm (P < .01). In end-systole, there were no differences between individual expert and AI performances. CONCLUSIONS AND CLINICAL IMPORTANCE: An artificial intelligence network can be trained to adequately measure linear LV dimensions, with performance indistinguishable from that of experts.

Automated mitral inflow Doppler peak velocity measurement using deep learning.

Doppler echocardiography is a widely utilised non-invasive imaging modality for assessing the functionality of heart valves, including the mitral valve. Manual assessments of Doppler traces by clinicians introduce variability, prompting the need for automated solutions. This study introduces an innovative deep learning model for automated detection of peak velocity measurements from mitral inflow Doppler images, independent from Electrocardiogram information. A dataset of Doppler images annotated by multiple expert cardiologists was established, serving as a robust benchmark. The model leverages heatmap regression networks, achieving 96% detection accuracy. The model discrepancy with the expert consensus falls comfortably within the range of inter- and intra-observer variability in measuring Doppler peak velocities. The dataset and models are open-source, fostering further research and clinical application.

Automated Left Ventricular Dimension Assessment Using Artificial Intelligence Developed and Validated by a UK-Wide Collaborative.

BACKGROUND: requires training and validation to standards expected of humans. We developed an online platform and established the Unity Collaborative to build a dataset of expertise from 17 hospitals for training, validation, and standardization of such techniques. METHODS: The training dataset consisted of 2056 individual frames drawn at random from 1265 parasternal long-axis video-loops of patients undergoing clinical echocardiography in 2015 to 2016. Nine experts labeled these images using our online platform. From this, we trained a convolutional neural network to identify keypoints. Subsequently, 13 experts labeled a validation dataset of the end-systolic and end-diastolic frame from 100 new video-loops, twice each. The 26-opinion consensus was used as the reference standard. The primary outcome was precision SD, the SD of the differences between AI measurement and expert consensus. RESULTS: In the validation dataset, the AI's precision SD for left ventricular internal dimension was 3.5 mm. For context, precision SD of individual expert measurements against the expert consensus was 4.4 mm. Intraclass correlation coefficient between AI and expert consensus was 0.926 (95% CI, 0.904-0.944), compared with 0.817 (0.778-0.954) between individual experts and expert consensus. For interventricular septum thickness, precision SD was 1.8 mm for AI (intraclass correlation coefficient, 0.809; 0.729-0.967), versus 2.0 mm for individuals (intraclass correlation coefficient, 0.641; 0.568-0.716). For posterior wall thickness, precision SD was 1.4 mm for AI (intraclass correlation coefficient, 0.535 [95% CI, 0.379-0.661]), versus 2.2 mm for individuals (0.366 [0.288-0.462]). We present all images and annotations. This highlights challenging cases, including poor image quality and tapered ventricles. CONCLUSIONS: Experts at multiple institutions successfully cooperated to build a collaborative AI. This performed as well as individual experts. Future echocardiographic AI research should use a consensus of experts as a reference. Our collaborative welcomes new partners who share our commitment to publish all methods, code, annotations, and results openly.

Retrospective analysis of tricuspid valve repair using a novel surgical technique: A 7-year single-surgeon experience.

OBJECTIVES: Tricuspid annuloplasty is the optimal surgical repair technique for tricuspid regurgitation which improves mortality and morbidity. Ring annuloplasties is the techniques of choice. Here, we evaluate the efficacy and durability of a new method of interrupted pledgeted suture annuloplasty. METHODS: Between 2011 and 2018, 39 eligible patients underwent tricuspid valve repair using this novel technique. Indication for repair was a grade of regurgitation at moderate or greater, or an annular diameter >40 mm. Patients were assessed both preoperatively and postoperatively by echocardiogram. Follow-up results were split into the first postoperative echocardiogram and most recent postoperative echocardiogram undertaken. RESULTS: There were two in-hospital mortalities and two patients required permanent pacemaker implantation following surgery. At the time of the first postoperative echocardiogram undertaken (median 3 months postoperatively), freedom from moderate-severe regurgitation was 92.3%. At the time of the most recent postoperative echocardiogram undertaken (median 11 months postoperatively); none or mild regurgitation was detected in 24 patients (61.5%), mild-moderate in 11 (28.2%) and moderate-severe in 4 (10.3%) patients. Freedom from moderate-severe regurgitation was 89.7%. Postoperative grade of regurgitation was significantly reduced from preoperative grades (p < 0.001). CONCLUSION: Initial and midterm results of our technique show a good durability of repair. We have demonstrated recurrence rates of regurgitation equal and superior to current forms of suture annuloplasty published in the literature. This novel method of suture annuloplasty can be considered in the surgical repertoire of tricuspid valve repair techniques.

Targeting the Nuclear Import Receptor Kpnß1 as an Anticancer Therapeutic.

Karyopherin beta 1 (Kpnß1) is a nuclear transport receptor that imports cargoes into the nucleus. Recently, elevated Kpnß1 expression was found in certain cancers and Kpnß1 silencing with siRNA was shown to induce cancer cell death. This study aimed to identify novel small molecule inhibitors of Kpnß1, and determine their anticancer activity. An in silico screen identified molecules that potentially bind Kpnß1 and Inhibitor of Nuclear Import-43, INI-43 (3-(1H-benzimidazol-2-yl)-1-(3-dimethylaminopropyl)pyrrolo[5,4-b]quinoxalin-2-amine) was investigated further as it interfered with the nuclear localization of Kpnß1 and known Kpnß1 cargoes NFAT, NFκB, AP-1, and NFY and inhibited the proliferation of cancer cells of different tissue origins. Minimum effect on the proliferation of noncancer cells was observed at the concentration of INI-43 that showed a significant cytotoxic effect on various cervical and esophageal cancer cell lines. A rescue experiment confirmed that INI-43 exerted its cell killing effects, in part, by targeting Kpnß1. INI-43 treatment elicited a G2-M cell-cycle arrest in cancer cells and induced the intrinsic apoptotic pathway. Intraperitoneal administration of INI-43 significantly inhibited the growth of subcutaneously xenografted esophageal and cervical tumor cells. We propose that Kpnß1 inhibitors could have therapeutic potential for the treatment of cancer. Mol Cancer Ther; 15(4); 560-73. ©2016 AACR.

The nuclear import receptor Kpnß1 and its potential as an anticancer therapeutic target.

Many proteins require transport across the nuclear envelope, the physical barrier separating the nucleus from the cytoplasm. Karyopherin ß (Kpnß1) proteins are the major nuclear receptor proteins in the cell that cargo proteins across the nuclear envelope, allowing them to enter and exit the cell nucleus. Karyopherin ß1, a major nuclear import receptor, plays an integral role in importing transcription factors, cell signaling proteins, cell cycle proteins, and so forth, into the nucleus, thus playing a crucial role in maintaining normal cell homeostasis. However, cancer cells appear to differentially regulate the expression of the Karyopherin ß proteins, presumably in order to maintain increased nuclear transport rates, thus implicating this protein family as a target for cancer therapy. The role of Kpnß1 in cancer is only now being elucidated, and recent work points to its potential usefulness as an anti-cancer target.