Artificial intelligence in pancreatic cancer

Theranostics, Journal Year: 2022, Volume and Issue: 12(16), P. 6931 - 6954

Published: Jan. 1, 2022

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%.The pancreatic patients diagnosed early screening have median nearly ten years, compared 1.5 years for those not screening.Therefore, diagnosis and treatment are particularly critical.However, as rare general cost high, accuracy existing tumor markers enough, efficacy methods exact.In terms diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, other aspects, then lesions early.At same time, algorithm also be used to predict recurrence risk, metastasis, therapy response which could affect prognosis.In addition, widely in health records, estimating imaging parameters, developing computer-aided systems, etc. Advances AI applications will require concerted effort among clinicians, basic scientists, statisticians, engineers.Although it has some limitations, play an essential role overcoming foreseeable future due its mighty computing power.

Language: Английский

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Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 72518 - 72536

Published: Jan. 1, 2023

Around the world, brain tumors are becoming leading cause of mortality. The inability to undertake a timely tumor diagnosis is primary this pandemic. Brain cancer crucial procedure that relies on expertise and experience doctor. Radiologists must use an automated classification model find cancers. current model's accuracy has be improved get suitable therapies. can consult various computer-aided diagnostic (CAD) models in literature medical imaging assist them with their patients. Previous research widely used CNN for detection classification, which typically require large datasets. This proposed Caps-VGGNet hybrid model, integrates CapsNet VGGNet by adding layers VGGNet. presented addresses challenge requiring datasets automatically extracting classifying features. suggested algorithm's effectiveness was assessed using Brats-2020 Brats-2019 dataset, contains high-quality images tumors. Compared other conventional models, empirical outcomes indicate it exhibited highest level superior efficacy terms accuracy, specificity, sensitivity. Specifically, attained 0.99, specificity sensitivity 0.98 Brats20 dataset.

Language: Английский

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Interpretability Research of Deep Learning: A Literature Survey

Information Fusion, Journal Year: 2024, Volume and Issue: 115, P. 102721 - 102721

Published: Oct. 9, 2024

Language: Английский

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Challenges and applications of artificial intelligence in infectious diseases and antimicrobial resistance

npj Antimicrobials and Resistance, Journal Year: 2025, Volume and Issue: 3(1)

Published: Jan. 7, 2025

Artificial intelligence (AI) has transformed infectious disease control, enhancing rapid diagnosis and antibiotic discovery. While conventional tests delay diagnosis, AI-driven methods like machine learning deep assist in pathogen detection, resistance prediction, drug These tools improve stewardship identify effective compounds such as antimicrobial peptides small molecules. This review explores AI applications diagnostics, therapy, discovery, emphasizing both strengths areas needing improvement.

Language: Английский

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Machine Learning Empowering Personalized Medicine: A Comprehensive Review of Medical Image Analysis Methods

Electronics, Journal Year: 2023, Volume and Issue: 12(21), P. 4411 - 4411

Published: Oct. 25, 2023

Artificial intelligence (AI) advancements, especially deep learning, have significantly improved medical image processing and analysis in various tasks such as disease detection, classification, anatomical structure segmentation. This work overviews fundamental concepts, state-of-the-art models, publicly available datasets the field of imaging. First, we introduce types learning problems commonly employed then proceed to present an overview used methods, including convolutional neural networks (CNNs), recurrent (RNNs), generative adversarial (GANs), with a focus on task they are solving, object detection/localization, segmentation, generation, registration. Further, highlight studies conducted application areas, encompassing neurology, brain imaging, retinal analysis, pulmonary digital pathology, breast cardiac bone abdominal musculoskeletal The strengths limitations each method carefully examined, paper identifies pertinent challenges that still require attention, limited availability annotated data, variability images, interpretability issues. Finally, discuss future research directions particular developing explainable methods integrating multi-modal data.

Language: Английский

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Fuzzy inference system with interpretable fuzzy rules: Advancing explainable artificial intelligence for disease diagnosis—A comprehensive review

Information Sciences, Journal Year: 2024, Volume and Issue: 662, P. 120212 - 120212

Published: Jan. 26, 2024

Interpretable artificial intelligence (AI), also known as explainable AI, is indispensable in establishing trustable AI for bench-to-bedside translation, with substantial implications human well-being. However, the majority of existing research this area has centered on designing complex and sophisticated methods, regardless their interpretability. Consequently, main prerequisite implementing trustworthy medical domains not been met. Scientists have developed various explanation methods interpretable AI. Among these fuzzy rules embedded a inference system (FIS) emerged novel powerful tool to bridge communication gap between humans advanced machines. there few reviews use FISs diagnosis. In addition, application different kinds multimodal data received insufficient attention, despite potential appropriate methodologies available datasets. This review provides fundamental understanding interpretability rules, conducts comparative analyses other handling three major types (i.e., sequence signals, images, tabular data), offers insights into rule scenarios recommendations future research.

Language: Английский

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Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 155, P. 102935 - 102935

Published: July 26, 2024

Deep learning (DL) in orthopaedics has gained significant attention recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation osteoarthritis severity. The utilisation is expected increase, owing its ability present accurate diagnoses more efficiently than traditional methods many scenarios. This reduces the time cost diagnosis for patients surgeons. To our knowledge, no exclusive study comprehensively reviewed all aspects currently used practice. review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, Web between 2017 2023. authors begin with motivation orthopaedics, enhance treatment planning. then covers various applications detection supraspinatus tears MRI, osteoarthritis, prediction types arthroplasty implants, age assessment, joint-specific soft tissue disease. We also examine challenges implementing scarcity data train lack interpretability, as well possible solutions these common pitfalls. Our work highlights requirements achieve trustworthiness outcomes generated by DL, need accuracy, explainability, fairness models. pay particular fusion techniques one ways increase trustworthiness, which been address multimodality orthopaedics. Finally, we approval set forth US Food Drug Administration enable use applications. As such, aim function guide researchers develop reliable application tasks scratch market.

Language: Английский

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Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

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Artificial intelligence/machine learning for epilepsy and seizure diagnosis

Epilepsy & Behavior, Journal Year: 2024, Volume and Issue: 155, P. 109736 - 109736

Published: April 17, 2024

Language: Английский

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Cloud and IoT based Smart Agent-driven Simulation of Human Gait for Detecting Muscles Disorder

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e42119 - e42119

Published: Jan. 1, 2025

Motion disorders affect a significant portion of the global population. While some symptoms can be managed with medications, these treatments often impact all muscles uniformly, not just affected ones, leading to potential side effects including involuntary movements, confusion, and decreased short-term memory. Currently, there is no dedicated application for differentiating healthy from abnormal ones. Existing analysis applications, designed other purposes, lack essential software engineering features such as user-friendly interface, infrastructure independence, usability learning ability, cloud computing capabilities, AI-based assistance. This research proposes computer-based methodology analyze human motion differentiate between unhealthy muscles. First, an IoT-based approach proposed digitize using smartphones instead hardly accessible wearable sensors markers. The data then simulated neuromusculoskeletal system. An agent-driven modeling method ensures naturalness, accuracy, interpretability simulation, incorporating neuromuscular details Henneman's size principle, action potentials, motor units, biomechanical principles. results are provided medical clinical experts aid in further investigation. Additionally, deep learning-based ensemble framework assist simulation results, offering both accuracy interpretability. A graphical interface enhances application's usability. Being fully cloud-based, infrastructure-independent accessed on smartphones, PCs, devices without installation. strategy only addresses current challenges treating but also paves way simulations by considering scientific computational requirements.

Language: Английский

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