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

Mar 31, 2023Journal of medical Internet research

Using Artificial Intelligence to Predict and Detect Pancreatic Cancer Early

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Abstract

Of the 1185 publications reviewed, 30 studies focused on AI for the prediction and early diagnosis of pancreatic cancer.

AI techniques were primarily used for diagnosing pancreatic cancer in 47% of the included studies.
Radiological images were the most common data type used, appearing in 47% of the articles.
Most studies relied on datasets with fewer than 1000 samples, indicating a potential limitation in data size (37%).
Deep learning models were the most frequently employed AI methods, with convolutional neural networks being the dominant algorithm (60%).
The studies reported a high accuracy level of 99% when using support vector machines, decision trees, and k-means clustering algorithms.
Validation approaches included k-fold cross-validation and external validation, each used in 33% of the studies.

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

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