An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Cardobi, Nicolò; Palù, Alessandro Dal; Pedrini, Federica; Beleù, Alessandro; Nocini, Riccardo; Robertis, Riccardo De; Ruzzenente, Andrea; Salvia, Roberto; Montemezzi, Stefania; D’Onofrio, Mirko

doi:10.3390/cancers13092162

Cited by 14 publications

(6 citation statements)

References 69 publications

(74 reference statements)

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“…For segmentation, there are some public datasets available for CT, like the LITS [56] and 3DIRCAD [101] but only CHAOS [102] for MRI. Increasing the amount of training data while providing multicentre acquisitions can boost the performance of AI models [103]. These multi-centre datasets must be heterogenous enough to eliminate biases relating to race, gender, ethnicity, and age.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Liver Transplant in Patients With Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

Pomohaci¹,

Grasu²,

Dumitru³

et al. 2023

Preprint

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Hepatocellular carcinoma is the most common primary malignant hepatic tumor and occurs most often in the setting of chronic liver disease. Liver transplantation is a curative treatment option and is an ideal solution because it solves the chronic underlying liver disorder while removing the malignant lesion. Because of organ shortages this treatment can only be applied to carefully selected patients according to clinical guidelines to minimize risk of recurrence. Artificial intelligence is an emerging technology with multiple applications in medicine with a predilection for domains that work with medical imaging like radiology. With the help of these technologies laborious tasks like segmentation can be automated and workflow in radiology departments can be improved. Other roles include in depth pixel-wise analysis of lesions by radiomics or deep learning in order to find new imaging criteria that allow better prediction of treatment response or risk of recurrence. Liver transplant is an ideal treatment for patients with hepatocellular carcinoma in the setting of chronic liver disease and artificial intelligence could provide solutions for improving the management of liver transplant candidates to improve survival.

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Section: Discussion and Limitationsmentioning

confidence: 99%

Liver Transplant in Patients With Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

Pomohaci¹,

Grasu²,

Dumitru³

et al. 2023

Preprint

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“…The efficiency of AI algorithms to perform repetitive tasks, such as segmentation have already been shown to outperform manual approaches in various clinical studies. A study by Winkel D. et al [ 123 , 124 ] showed that their fully automated liver segmentation algorithm using deep learning was able to achieve a mean processing time of 9.94 s, at least 20 times faster than manual segmentation with excellent agreement between the two approaches (intraclass correlation coefficient of 0.996). In a polycystic liver and kidney disease series of CT images, an artificial intelligence model segmented the liver parenchyma at 8333 slices/hour, compared to labour intensive manual segmentation by an expert clinician, which did not surpass 16 slices/hour, with a DSC of 0.96 [ 125 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Ong

Zhu

Zhang

et al. 2022

Cancers

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Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.

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“…The liver is also a popular target for automated segmentation algorithms. Automatic segmentation of this organ is regarded as somewhat less challenging than that of the pancreas, with reported DSC scores typically in the > 0.90 range[ 35 ].…”

Section: Image Analysismentioning

confidence: 99%

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

Berbís¹,

Godino²,

Val³

et al. 2023

World J Gastroenterol

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Artificial intelligence (AI) has experienced substantial progress over the last ten years in many fields of application, including healthcare. In hepatology and pancreatology, major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images, where AI can generate accurate and reproducible imaging diagnosis, reducing the physicians’ workload. AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions. Furthermore, using radiomics, AI can introduce new quantitative information which is not visible to the human eye to radiological reports. AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas, such as neoplasms, chronic hepatic disease, or acute or chronic pancreatitis, among others. These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases, such as ultrasound, endoscopic ultrasonography, computerized tomography (CT), magnetic resonance imaging, and positron emission tomography/CT. However, AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient. AI can also be applied to choose the most convenient test prescription, to improve image quality or accelerate its acquisition, and to predict patient prognosis and treatment response. In this review, we summarize the current evidence on the application of AI to hepatic and pancreatic radiology, not only in regard to the interpretation of images, but also to all the steps involved in the radiological workflow in a broader sense. Lastly, we discuss the challenges and future directions of the clinical application of AI methods.

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An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

Cited by 14 publications

References 69 publications

Liver Transplant in Patients With Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

Liver Transplant in Patients With Hepatocarcinoma: Imaging Guidelines and Future Perspectives Using Artificial Intelligence

Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis

Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

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