Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising

Ladd, Antonio Mendoza; Diehl, David L.

doi:10.3748/wjg.v27.i13.1283

Cited by 19 publications

(13 citation statements)

References 67 publications

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“…They reported that imaging, blood-based assays, microbiome, and patients’ characteristics can be used for the early diagnosis of pancreatic cancer with the help of machine learning algorithms [ 9 ]. Mendoza Ladd and Diehl [ 46 ] published a literature review that explored the use of AI techniques in diagnosing pancreatic cancer. They focused on the analysis of endoscopic ultrasound images, CT images, and magnetic resonance images using AI algorithms.…”

Section: Discussionmentioning

confidence: 99%

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%

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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“…A combination of T2-W sequences and DWI has been shown to have similar accuracy to a conventional contrast-enhanced MRI protocol [213]. Pozzi-Mucelli et al showed that that an abbreviated protocol MRI is more economical, and provides equivalent clinical data for patient surveillance [214]. No specific diagnostic study protocols are recommended for the diagnosis or surveillance of PCN patients, since there is widespread published data and a lack of dedicated comparative studies [198].…”

Section: Pancreatic Cystic Neoplasm Diagnostic Managementmentioning

confidence: 99%

Section: Pancreatic Cystic Neoplasm Diagnostic Managementmentioning

confidence: 99%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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“…Bilateral filtering is mainly an improvement for Gaussian filtering, and considers the influence of distance and pixel value on filtering. The product of the Gaussian function and the image pixel value information is used as the filter weight coefficient, and the optimized weight coefficient is then convolved with the image information (16). In bilateral filtering, the relationship between the output pixel value g(i,j) and the domain pixel value f(k,I) is:…”

Section: Depth Image Inpainting Based On Bilateral Filteringmentioning

confidence: 99%

Physical education movement and comprehensive health quality intervention under the background of artificial intelligence

Zhang

Jin

Duan

2022

Front. Public Health

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The application of artificial intelligence has realized the transformation of people's production and lifestyle, and also promoted the progress of physical education and comprehensive health quality. The application of artificial intelligence in the current physical education movement is increasing. By utilizing its advanced method of virtual simulation technology, the purpose of this paper is to realize the interventional research on the physical education movement and comprehensive health quality in the environment of artificial intelligence. This paper proposes to use the virtual simulation technology and Kinect algorithm in artificial intelligence to design the virtual sports simulation teaching mode. The functional module design part where the Kinect algorithm helps the teaching of virtual sports simulation experiments, which is helpful to analyze and solve the objective system imbalance and ecological imbalance in online physical education teaching. By using the principles and rules of the Mean Shift image segmentation algorithm for reference, the investigation and research on the comprehensive health quality of students are carried out, so as to realize the ecologicalization of the virtual sports school. In the investigation and research on the comprehensive quality of students, the results show that the overall quality of these students who has reached the level of qualified or unqualified is accounting for about 30% of the total number. It is worth noting that in terms of scientific and cultural quality, only 43.34% of all students have excellent grades. It can be seen that the important training goal of current school research is how to use reasonable and effective methods and strategies to improve students' scientific and cultural level, and improve students' other comprehensive scores at the same time.

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Artificial intelligence for early detection of pancreatic adenocarcinoma: The future is promising

Cited by 19 publications

References 67 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

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Physical education movement and comprehensive health quality intervention under the background of artificial intelligence

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