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

Jan, Zainab; Assadi, Farah El; Abd‐Alrazaq, Alaa; Jithesh, Puthen V.

doi:10.2196/44248

Cited by 10 publications

(1 citation statement)

References 48 publications

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“…Several studies have investigated the use of AI for the early detection of chronic diseases such as cardiovascular disease, diabetes, cancer, and infectious diseases [14,15,[45][46][47][48][49][50][51][52]. These research efforts highlight the ability of AI to significantly improve public health outcomes by allowing health professionals to detect chronic diseases in their early stages when interventions are most effective.…”

Section: Early Detection Of Chronic and Infectious Diseasesmentioning

confidence: 99%

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Badidi

2023

Future Internet

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Edge AI, an interdisciplinary technology that enables distributed intelligence with edge devices, is quickly becoming a critical component in early health prediction. Edge AI encompasses data analytics and artificial intelligence (AI) using machine learning, deep learning, and federated learning models deployed and executed at the edge of the network, far from centralized data centers. AI enables the careful analysis of large datasets derived from multiple sources, including electronic health records, wearable devices, and demographic information, making it possible to identify intricate patterns and predict a person’s future health. Federated learning, a novel approach in AI, further enhances this prediction by enabling collaborative training of AI models on distributed edge devices while maintaining privacy. Using edge computing, data can be processed and analyzed locally, reducing latency and enabling instant decision making. This article reviews the role of Edge AI in early health prediction and highlights its potential to improve public health. Topics covered include the use of AI algorithms for early detection of chronic diseases such as diabetes and cancer and the use of edge computing in wearable devices to detect the spread of infectious diseases. In addition to discussing the challenges and limitations of Edge AI in early health prediction, this article emphasizes future research directions to address these concerns and the integration with existing healthcare systems and explore the full potential of these technologies in improving public health.

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Section: Early Detection Of Chronic and Infectious Diseasesmentioning

confidence: 99%

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Badidi

2023

Future Internet

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IANFIS: a machine learning–based optimized technique for the classification and segmentation of pancreatic cancer

Dodda,

Muneeswari

2024

Res. Biomed. Eng.

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Nosocomial transmission of Aspergillus flavus in a neonatal intensive care unit: Long-term persistence in environment and interest of MALDI–ToF mass-spectrometry coupled with convolutional neural network for rapid clone recognition

Mohammad,

Huguenin,

Lefebvre

et al. 2023

Medical Mycology

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Aspergillosis of the new-born remains a rare but severe disease. We report four cases of primary cutaneous A. flavus infections in premature new-borns linked to incubators contamination by putative clonal strains. Our objective was to evaluate the ability of MALDI-TOF coupled to Convolutional Neural Network (CNN) for clone recognition in a context where only a very small number of strains are available for machine learning. Clinical and environmental A. flavus isolates (n = 64) were studied, 15 were epidemiologically related to the four cases. All strains were typed using microsatellite length polymorphism. We found a common genotype for 9/15 related strains. The isolates of this common genotype were selected to obtain a training dataset (6 clonal isolates/25 non-clonal) and a test dataset (3 clonal isolates/31 non-clonal), and spectra were analysed with a simple CNN model. On the test dataset using CNN model, all 31 non clonal isolates were correctly classified, 2/3 clonal isolates were unambiguously correctly classified whereas the third strain was undetermined (i.e the CNN model was unable to discriminate between GT8 and non-GT8). Clonal strains of A. flavus have persisted in the neonatal intensive care unit for several years. Indeed, two strains of A. flavus isolated from incubators in September 2007, are identical to the strain responsible for the second case that occurred 3 years later. MALDI-TOF is a promising tool for detecting clonal isolates of A. flavus using CNN even with a limited training set for limited cost and handling time.

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

Cited by 10 publications

References 48 publications

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

IANFIS: a machine learning–based optimized technique for the classification and segmentation of pancreatic cancer

Nosocomial transmission of Aspergillus flavus in a neonatal intensive care unit: Long-term persistence in environment and interest of MALDI–ToF mass-spectrometry coupled with convolutional neural network for rapid clone recognition

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