Search

Background: Artificial intelligence (AI) models have been applied in various medical imaging modalities and surgical disciplines, however the current status and progress of ultrasound-based AI models within hepatopancreatobiliary surgery have not been evaluated in literature. Therefore, this review aimed to provide an overview of ultrasound-based AI models used for hepatopancreatobiliary surgery, evaluating current advancements, validation, and predictive accuracies. Method: Databases PubMed, EMBASE, Cochrane, and Web of Science were searched for studies using AI models on ultrasound for patients undergoing hepatopancreatobiliary surgery. To be eligible for inclusion, studies needed to apply AI methods on ultrasound imaging for patients undergoing hepatopancreatobiliary surgery. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. Results: AI models have been primarily used within hepatopancreatobiliary surgery, to predict tumor recurrence, differentiate between tumoral tissues, and identify lesions during ultrasound imaging. Most studies have combined radiomics with convolutional neural networks, with AUCs up to 0.98. Conclusion: Ultrasound-based AI models have demonstrated promising accuracies in predicting early tumoral recurrence and even differentiating between tumoral tissue types during and after hepatopancreatobiliary surgery. However, prospective studies are required to evaluate if these results will remain consistent and externally valid. [ABSTRACT FROM AUTHOR]

Purpose: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. Methods: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). Results: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. Conclusion: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches. [ABSTRACT FROM AUTHOR]