Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video

We present an automatic method to describe clinically useful information about scanning, and to guide image interpretation in ultrasound (US) videos of the fetal heart. Our method is able to jointly predict the visibility, viewing plane, location and orientation of the fetal heart at the frame level. The contributions of the paper are three-fold: (i) a convolutional neural network architecture is developed for a multi-task prediction, which is computed by sliding a 3x3 window spatially through convolutional maps. (ii) an anchor mechanism and Intersection over Union (IoU) loss are applied for improving localization accuracy. (iii) a recurrent architecture is designed to recursively compute regional convolutional features temporally over sequential frames, allowing each prediction to be conditioned on the whole video. This results in a spatial-temporal model that precisely describes detailed heart parameters in challenging US videos. We report results on a real-world clinical dataset, where our method achieves performance on par with expert annotations.Comment: To appear in MICCAI, 201

A framework for analysis of linear ultrasound videos to detect fetal presentation and heartbeat.

Confirmation of pregnancy viability (presence of fetal cardiac activity) and diagnosis of fetal presentation (head or buttock in the maternal pelvis) are the first essential components of ultrasound assessment in obstetrics. The former is useful in assessing the presence of an on-going pregnancy and the latter is essential for labour management. We propose an automated framework for detection of fetal presentation and heartbeat from a predefined free-hand ultrasound sweep of the maternal abdomen. Our method exploits the presence of key anatomical sonographic image patterns in carefully designed scanning protocols to develop, for the first time, an automated framework allowing novice sonographers to detect fetal breech presentation and heartbeat from an ultrasound sweep. The framework consists of a classification regime for a frame by frame categorization of each 2D slice of the video. The classification scores are then regularized through a conditional random field model, taking into account the temporal relationship between the video frames. Subsequently, if consecutive frames of the fetal heart are detected, a kernelized linear dynamical model is used to identify whether a heartbeat can be detected in the sequence. In a dataset of 323 predefined free-hand videos, covering the mother's abdomen in a straight sweep, the fetal skull, abdomen, and heart were detected with a mean classification accuracy of 83.4%. Furthermore, for the detection of the heartbeat an overall classification accuracy of 93.1% was achieved

Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning

Funded by the Spanish Ministry of Economic Affairs and Digital Transformation (Project MIA.2021.M02.0005 TARTAGLIA, from the Recovery, Resilience, and Transformation Plan financed by the European Union through Next Generation EU funds). TARTAGLIA takes place under the R&D Missions in Artificial Intelligence program, which is part of the Spain Digital 2025 Agenda and the Spanish National Artificial Intelligence Strategy.Ultrasound (US) is a flexible imaging modality used globally as a first-line medical exam procedure in many different clinical cases. It benefits from the continued evolution of ultrasonic technologies and a well-established US-based digital health system. Nevertheless, its diagnostic performance still presents challenges due to the inherent characteristics of US imaging, such as manual operation and significant operator dependence. Artificial intelligence (AI) has proven to recognize complicated scan patterns and provide quantitative assessments for imaging data. Therefore, AI technology has the potential to help physicians get more accurate and repeatable outcomes in the US. In this article, we review the recent advances in AI-assisted US scanning. We have identified the main areas where AI is being used to facilitate US scanning, such as standard plane recognition and organ identification, the extraction of standard clinical planes from 3D US volumes, and the scanning guidance of US acquisitions performed by humans or robots. In general, the lack of standardization and reference datasets in this field makes it difficult to perform comparative studies among the different proposed methods. More open-access repositories of large US datasets with detailed information about the acquisition are needed to facilitate the development of this very active research field, which is expected to have a very positive impact on US imaging.Depto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasTRUEMinistry of Economic Affairs and Digital Transformation from the Recovery, Resilience, and Transformation PlanNext Generation EU fundspu

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Vessel recognition in ultrasound images using machine learning techniques

Purpose: Ultrasound is an imaging modality that is commonly used during cardiovascular surgeries globally. The purpose of this thesis is to investigate how machine learning techniques can be used to identify vessel properties and probe orientation in cardiac ultrasound images. The ultimate goal is developing a machine learning algorithm that can automatically recognize vessels in the region of interest with high mean average precision, identify vessel orientation, and run in near real-time. Method: This thesis present a thoroughly data exploration of ultrasound images acquired from a multicenter study. A pilot study of three different object detection models; Yolo, RetinaNet and EfficientDet, was done to find the best model fit for the dataset in the thesis. The three object detection models were trained, tuned and evaluated on the ultrasound data. The object detection model that performed the best after the pilot study was explored further. Yolo outperformed the other models and was therefore chosen as the object detection model for the final study. To overcome the dataset's class imbalance and size problem, data augmentation, resizing and upscaling of the ultrasound images were employed. The resulting data was used to train multiple yolo models with varying hyperparameter tunings. Model selection was then performed on these trained models, and the final model was evaluated on test data. Results: The final model achieved an overall mean average precision at 50\% at 71.77\%. The vessel orientation achieved a mean average precision at 64.6\% for the longitudinal orientation and 75.8\% for the transversal orientation. The model found it easier to locate the aorta compared to the anastomosis, which proved to be more challenging. The speed of the inference of all of these task was 5.6 milliseconds. Although the overall mean average precision was lower than the objective in this thesis, the model excelled in terms of speed. Conclusion: In conclusion, this thesis explored the application of machine learning techniques on ultrasound data for vessel recognition and orientation. Although the final model did not improve the state of the art, the research from this master thesis can serve as a starting point for future reasearch in the field. It represents pioneering work in utilizing a multicenter dataset for machine learning on ultrasound images, providing valuable groundwork and shedding light on the feasibility and potential of machine learning in intraoperative ultrasound.Masteroppgave i medisinsk teknologiMTEK39

Pediatric emergency medicine point-of-care ultrasound: summary of the evidence.

The utility of point-of-care ultrasound is well supported by the medical literature. Consequently, pediatric emergency medicine providers have embraced this technology in everyday practice. Recently, the American Academy of Pediatrics published a policy statement endorsing the use of point-of-care ultrasound by pediatric emergency medicine providers. To date, there is no standard guideline for the practice of point-of-care ultrasound for this specialty. This document serves as an initial step in the detailed how to and description of individual point-of-care ultrasound examinations. Pediatric emergency medicine providers should refer to this paper as reference for published research, objectives for learners, and standardized reporting guidelines