/pdf/a-review-on-deep-learning-algorithms-for-fetal-ultrasound-ejqth3ya.pdf

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.

/pdf/artificial-intelligence-in-obstetric-ultrasound-an-update-1l1jqa1ae4.pdf

Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications.

Ultrasound is one of the most ubiquitous imaging modalities in clinical practice It is cheap, does not require ionizing radiation and can be performed at the bedside, making it the most commonly utilized imaging technique in pregnancy Despite these advantages, it does have some drawbacks such as relatively low imaging quality, low contrast, and high variability With these constraints, automating the interpretation of ultrasound images is challenging However, successful automated identification of structures within 3D ultrasound volumes has the potential to revolutionize clinical practice For example, a small placental volume in the first trimester has been shown to be correlated to adverse outcome later in pregnancy If the placenta could be segmented reliably and automatically from a static 3D ultrasound volume, it would facilitate the use of its estimated volume, and other morphological metrics, as part of a screening test for increased risk of pregnancy complications potentially improving clinical outcomes Recently, deep learning has emerged, achieving state-of-the-art performance in various research fields, notably medical image analysis involving classification, segmentation, object detection, and tracking tasks Due to its increased performance with large datasets, it has gained great interest in medical imaging applications In this review, we present an overview of deep learning methods applied to ultrasound in pregnancy, introducing their architectures and analyzing their strategies We then present some common problems and provide some perspectives into potential future research

Deep Learning strategies for Ultrasound in Pregnancy.

Artificial intelligence (AI) is defined as the development of computer systems to perform tasks normally requiring human intelligence. A subset of AI, known as machine learning (ML), takes this further by drawing inferences from patterns in data to ‘learn’ and ‘adapt’ without explicit instructions meaning that computer systems can ‘evolve’ and hopefully improve without necessarily requiring external human influences. The potential for this novel technology has resulted in great interest from the medical community regarding how it can be applied in healthcare. Within radiology, the focus has mostly been for applications in oncological imaging, although new roles in other subspecialty fields are slowly emerging. In this scoping review, we performed a literature search of the current state-of-the-art and emerging trends for the use of artificial intelligence as applied to fetal magnetic resonance imaging (MRI). Our search yielded several publications covering AI tools for anatomical organ segmentation, improved imaging sequences and aiding in diagnostic applications such as automated biometric fetal measurements and the detection of congenital and acquired abnormalities. We highlight our own perceived gaps in this literature and suggest future avenues for further research. It is our hope that the information presented highlights the varied ways and potential that novel digital technology could make an impact to future clinical practice with regards to fetal MRI.

Artificial intelligence applied to fetal MRI: A scoping review of current research

https://doi.org/10.1016/j.compbiomed.2022.105623

Learning deep neural networks' architectures using differential evolution. Case study: Medical imaging processing

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.


 
 
 Around the globe, over 850 million patients suffer from chronic kidney disease (CKD). These have associated with high mortality rates, in particular when undergoing renal replacement therapies (RRT) such as dialysis, reaching up to 10% a year, and therefore, are considered of a fragile status. CKD is also associated with cardiovascular complications that can cause mutual aggravation. Available clinical guidelines identify certain risk factors and predictive models, but those have not been tested and validated successfully for renal patients and consequently, there is for the identification of predictive factors and the prediction of mortality. This is caused by the limitations of current methodologies and statistics: current models simplify complex relationships by assuming a linear relation between risk factors and certain events, and so, there is a need for a new approach. Over the last years, a rise in Artificial Intelligence and Machine Learning has been seen, presenting an alternative for the first time. This project aimed to study the performance of different ML algorithms for the prediction of mortality and the identification of risk factors for CKD patients.
 
 
 
 Design: Retrospective analysis of a historical cohort from the Register of Renal Patients of Catalonia (RMRC) and the Catalan Agency for Health Quality and Evaluation. Group of 10 473 patients with CKD stages from first to RRT. Follow-up of 11 years, from January 2010 to December 2020. Inclusion criteria: ˃18 years. Training of an Extreme Gradient Boosting model, and comparison with other algorithms for the prediction of mortality at different times, using different follow-up periods for each patient.
 Methodology: Variables: i) Age, gender, body mass index, time for death (9), ii) Diagnoses (ICD-9/10) (26); iii) Laboratory variables (37) iv) All pharmacological treatments (46). For all executions, data was balanced using the SMOTETomek technique.
 Analysis:
 
 
 
 The patient sample presented a mean of 68.2 ± 12.9 years and 65.8% were female and 34.2% male. Different follow-up and time windows were tested and the best results were obtained when using a 2-year period follow-up and a 4-year mortality prediction. The Area Under the Curve values obtained for each model were: XBGClassifier (0.89), LGBM Classifier (0.90), CatBoost Classifier (0.91). The 10 variables with major relevance according to the XBGClassifier (54.65% of the total weight of the 71 variables) and in this order, are cardiopathy, advanced chronic kidney disease, vasculopathy, age, neoplasia, transplant, digestive pathology, estimated glomerular filtration rate, high blood pressure. The results presented in Figure and Table correspond to the mean obtained for the 5-folds of the Cross-Validation.
 
 
 
 Machine Learning techniques suppose an alternative to classical statistical methods, with a high predictive capacity for mortality. The possibility of generating algorithms with real-world data can allow the individualization of the mortality risk as well as the predictive factors.


/pdf/mo463-machine-learning-based-prediction-of-mortality-and-2xha6vve.pdf

MO463: Machine Learning-Based Prediction of Mortality and Risk Factors in Patients With Chronic Kidney Disease Developed With Data From 10000 Patients Over 11 Years

Núria Monill

Papers

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.

MO463: Machine Learning-Based Prediction of Mortality and Risk Factors in Patients With Chronic Kidney Disease Developed With Data From 10000 Patients Over 11 Years