A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Mervak, Benjamin M.; Fried, Jessica G.; Wasnik, Ashish P.

doi:10.3390/diagnostics13182889

Cited by 4 publications

(2 citation statements)

References 89 publications

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Section: Discussionmentioning

confidence: 88%

“…Recent literature reviews have shown the emergence of various US-based algorithms designed to differentiate between benign and malignant focal liver lesions (FLLs). These have demonstrated accuracy levels comparable to those of radiologists (27). Yang et al designed a deep convolutional neural network (DCNN-US) to aid radiologists in distinguishing between benign and malignant FLLs using US.…”

Section: Ensemble Learning Was Effective For Phc Diagnosismentioning

confidence: 99%

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Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Wang,

Zhuang,

et al. 2024

Front. Oncol.

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Primary hepatocellular carcinoma (PHC) is associated with high rates of morbidity and malignancy in China and throughout the world. In clinical practice, a combination of ultrasound and alpha-fetoprotein (AFP) measurement is frequently employed for initial screening. However, the accuracy of this approach often falls short of the desired standard. Consequently, this study aimed to investigate the enhancement of precision of preliminary detection of PHC by ensemble learning techniques. To achieve this, 712 patients with PHC and 1887 healthy controls were enrolled for the assessment of four ensemble learning methods, namely, Random Forest (RF), LightGBM, Xgboost, and Catboost. A total of eleven characteristics, comprising nine serological indices and two demographic indices, were selected from the participants for use in detecting PHC. The findings identified an optimal feature subset consisting of eight features, namely AFP, albumin (ALB), alanine aminotransferase (ALT), platelets (PLT), age, alkaline phosphatase (ALP), hemoglobin (Hb), and body mass index (BMI), that achieved the highest classification accuracy of 96.62%. This emphasizes the importance of the collective use of these features in PHC diagnosis. In conclusion, the results provide evidence that the integration of serological and demographic indices together with ensemble learning models, can contribute to the precision of preliminary diagnosis of PHC.

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Section: Discussionmentioning

confidence: 88%

Section: Ensemble Learning Was Effective For Phc Diagnosismentioning

confidence: 99%

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Wang,

Zhuang,

et al. 2024

Front. Oncol.

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The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges

Yordanova

2024

EJMED

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Purpose: This article aims to provide insight and a better understanding of how the rapid development of artificial intelligence (AI) affects radiology practice and research. The article reviews existing scientific literature on the applications of AI in radiology and the opportunities and challenges they pose. Materials and Methods: This article uses available scientific literature on AI applications in radiology and its subspecialties from PubMed, Google Scholar and ScienceDirect. Results: The article finds that the applications of AI in radiology have grown significantly in the past decade, spanning across virtually all radiology subspecialties or areas of activity and all modalities of imaging such as the radiographer, computer tomography (CT) scan, magnetic resonance imaging (MRI), ultrasound and others. The AI applications in radiology present challenges related to testing and validation, professional uptake, and education and training. Nevertheless, artificial intelligence provides an opportunity for greater innovation in the field, improved accuracy, reduced burden of radiologists and better patient care among others. Conclusions: Despite the challenges it presents, artificial intelligence provides many worthwhile opportunities for the development of radiology and the next frontier in medicine.

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Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

Loper,

Makary

2024

Tomography

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Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of AI in abdominal imaging, with a focus on recent literature contributions. This work explores the diagnosis and characterization of hepatobiliary, pancreatic, gastric, colonic, and other pathologies. In addition, the role of AI has been observed to help differentiate renal, adrenal, and splenic disorders. Furthermore, workflow optimization strategies and quantitative imaging techniques used for the measurement and characterization of tissue properties, including radiomics and deep learning, are highlighted. An assessment of how these advancements enable more precise diagnosis, tumor description, and body composition evaluation is presented, which ultimately advances the clinical effectiveness and productivity of radiology. Despite the advancements of AI in abdominal imaging, technical, ethical, and legal challenges persist, and these challenges, as well as opportunities for future development, are highlighted.

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A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Cited by 4 publications

References 89 publications

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

Ensemble learning enhances the precision of preliminary detection of primary hepatocellular carcinoma based on serological and demographic indices

The Applications of Artificial Intelligence in Radiology: Opportunities and Challenges

Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging

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