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.

The Role of Biomarkers in Cardio-Oncology.

In the field of cardio-oncology, it is well recognised that despite the benefits of chemotherapy in treating and possibly curing cancer, it can cause catastrophic damage to bystander tissues resulting in a range of potentially of life-threatening cardiovascular toxicities, and leading to a number of damaging side effects including heart failure and myocardial infarction. Cardiotoxicity is responsible for significant morbidity and mortality in the long-term in oncology patients, specifically due to left ventricular dysfunction. There is increasing emphasis on the early use of biomarkers in order to detect the cardiotoxicity at a stage before it becomes irreversible. The most important markers of cardiac injury are cardiac troponin and natriuretic peptides, whilst markers of inflammation such as interleukin-6, C-reactive protein, myeloperoxidase, Galectin-3, growth differentiation factor-15 are under investigation for their use in detecting cardiotoxicity early. In addition, microRNAs, genome-wide association studies and proteomics are being studied as novel markers of cardiovascular injury or inflammation. The aim of this literature review is to discuss the evidence base behind the use of these biomarkers for the detection of cardiotoxicity.

Anandamide in primary sensory neurons: too much of a good thing?

The quest for possible targets for the development of novel analgesics has identified the activation of the cannabinoid type 1 (CB1) receptor outside the CNS as a potential means of providing relief from persistent pain, which currently constitutes an unmet medical need. Increasing tissue levels of the CB1 receptor endogenous ligand N-arachidonoylethanolamine (anandamide), by inhibiting anandamide degradation through blocking the anandamide-hydrolysing enzyme fatty acid amide hydrolase, has been suggested to be used to activate the CB1 receptor. However, recent clinical trials revealed that this approach does not deliver the expected relief from pain. Here, we discuss one of the possible reasons, the activation of the transient receptor potential vanilloid type 1 ion channel (TRPV1) on nociceptive primary sensory neurons (PSNs) by anandamide, which may compromise the beneficial effects of increased tissue levels of anandamide. We conclude that better design such as concomitant blocking of anandamide hydrolysis and anandamide uptake into PSNs, to inhibit TRPV1 activation, could overcome these problems.