Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Berbís, M. Álvaro; Aneiros‐Fernández, José; Olivares, F Javier Mendoza; Nava, Enrique; Luna, Antonio

doi:10.3748/wjg.v27.i27.4395

Cited by 10 publications

(4 citation statements)

References 94 publications

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“…However, the procedures are usually time-consuming and cost-intensive [14] . It is reported that some algorithms can assist in clinical images annotation, but the automatic method is particularly challenging in the context of the complicated abdominal anatomy [15] . Furthermore, the annotation of pixel-level for medical images requires professional expertise by experienced radiologists, thus it is laborious to obtain a large-scale labeled dataset of high-quality.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

Guo

Cao

Nie

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Background: Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. Method: In this study, we present a composite network that combines deep learning and unsupervised K-means clustering algorithm (RK-net) for automatic processing of medical images. Results: RK-net was more efficient in image refinement compared with manual screening and annotation. The training of a deep learning model for colorectal cancer diagnosis was accelerated by two times with utilization of RK-net-processed images. Better performance was observed in training loss and accuracy achievement as well. Conclusions: RK-net could be useful to refine medical images of the ever-expanding quantity and assist in subsequent construction of the artificial intelligence model.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

Guo

Cao

Nie

et al. 2023

IEEE J. Transl. Eng. Health Med.

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“…In recent years, artificial intelligence (AI) has shown promise in different areas of healthcare. The evaluation of medical images by machine learning (ML) approaches is a leading research field which, in gastroenterology, has applications in automatic analysis of different types of images, such as radiology, pathology, and endoscopy studies[ 5 ].…”

Section: Introductionmentioning

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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“…1,2 Excitement and expectations regarding the potential of AI to revolutionize healthcare have continued to build, as evidenced by the growing list of medical AI publications in the form of original research articles, review papers, health policy reports, white papers and consensus recommendations from professional societies, and coverage in the popular media. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] A recent survey of English-language articles indexed in PubMed showed a significant increase in the volume of medical AI research publications, from just 203 articles in 2005 to 12,563 in 2019. 3 Pathology has attracted growing attention as an image-rich specialty likely to be strongly impacted by recent advances in AI.…”

Section: Introductionmentioning

confidence: 99%

The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

Berbís¹,

McClintock

Bychkov

et al. 2022

Preprint

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Background: Artificial intelligence (AI) is rapidly fueling a fundamental transformation in the practice of pathology. However, the clinical integration of AI remains challenging, with no AI algorithms to date enjoying routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience. Methods: Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analyzed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus. Findings: Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) role of AI in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology. Interpretation: This is the first systematic consensus study detailing the expected short/mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to the successful clinical implementation of AI.

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Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Cited by 10 publications

References 94 publications

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis

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

The future of computational pathology: expectations regarding the anticipated role of artificial intelligence in pathology by 2030

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