Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis

Dumitrescu, Elena Adriana; Ungureanu, Bogdan Silviu; Cazacu, Irina M.; Florescu, Lucian Mihai; Streba, Liliana; Croitoru, Vlad Mihai; Sur, Daniel; Croitoru, Adina; Turcu-Știolică, Adina; Lungulescu, Cristian Virgil

doi:10.3390/diagnostics12020309

Cited by 29 publications

(19 citation statements)

References 36 publications

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“…They did not follow the PRISMA guidelines to ensure the reliability and transparency of the work [ 47 ]. Moreover, Dumitrescu et al [ 10 ] conducted a systematic review by following PRISMA guidelines and focused on the diagnostic value of AI-based ultrasound for the diagnosis of pancreatic cancer. Bradley et al [ 11 ] also followed PRISMA guidelines and critically analyzed the role of machine learning algorithms in clinical decision support systems for pancreatic cancer management.…”

Section: Discussionmentioning

confidence: 99%

“…First, they focused on the diagnosis of pancreatic cancer and AI, but they did not describe in detail the features of the AI algorithms used [ 9 ]. Second, they did not search relevant databases such as Google Scholar and Science Direct [ 10 , 11 ]. Third, they were restricted to diagnosing pancreatic cancer using only ultrasound [ 10 ] and imaging [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Second, they did not search relevant databases such as Google Scholar and Science Direct [ 10 , 11 ]. Third, they were restricted to diagnosing pancreatic cancer using only ultrasound [ 10 ] and imaging [ 12 ]. Fourth, they focused on the application of machine learning in clinical decision support systems for the management of pancreatic cancer [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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Background Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer. Objective This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature. Methods A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms. Conclusions This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Jan¹,

Assadi²,

Abd‐Alrazaq³

et al. 2023

J Med Internet Res

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“…With promising results, AI has been applied to help endoscopists in the EUS assessment of pancreatic lesions ( 61 – 63 ). Zhu et al.…”

Section: Discussionmentioning

confidence: 99%

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Yin

Zhang²,

Yang³

et al. 2022

Front. Oncol.

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PurposeWe evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021.MethodsPublications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis.ResultsA total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field.ConclusionThe research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.

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“…However, the literature reports chosen for the study had used images that compared normal and pancreatic cancer while some had tried to differentiate pancreatic cancer with CP. Similarly, the size of the cancerous tissues varied between the studies [ 69 ]. Therefore, additional studies are required to address if these differences could reflect in the prediction accuracy of the AI tool employed.…”

Section: Endoscopic Ultrasound (Eus)mentioning

confidence: 99%

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Hameed

Krishnan

2022

Cancers

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Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians’ treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

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Diagnostic Value of Artificial Intelligence-Assisted Endoscopic Ultrasound for Pancreatic Cancer: A Systematic Review and Meta-Analysis

Cited by 29 publications

References 36 publications

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

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