A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Diagnostics, Journal Year: 2023, Volume and Issue: 13(7), P. 1353 - 1353

Published: April 5, 2023

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in field cancer diagnosis recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements sequencing technologies, detection based on gene expression data improved over years. Different types affect different parts body ways. affects mouth, lip, upper throat known as oral cancer, which sixth most prevalent form worldwide. India, Bangladesh, China, United States, Pakistan are top five countries highest rates cavity disease lip cancer. major causes excessive use tobacco cigarette smoking. Many people's lives can be saved if (OC) detected early. Early identification could assist doctors providing better patient care effective treatment. OC screening may advance implementation artificial intelligence (AI) techniques. AI provide assistance oncology sector by accurately analyzing large dataset from several imaging modalities. This review deals during early stages for proper OC. Furthermore, performance evaluations DL ML models been carried out show model overcome difficult challenges associated cancerous lesions mouth. For this review, we followed rules recommended extension scoping reviews meta-analyses (PRISMA-ScR). Examining reference lists chosen articles helped us gather more details subject. Additionally, discussed AI's drawbacks its potential research There methods reducing risk factors, such alcohol, well immunization against HPV infection avoid or lessen burden disease. officious preventing diseases include training programs patients facilitating via high-risk populations

Language: Английский

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Innovation and challenges of artificial intelligence technology in personalized healthcare

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 16, 2024

As the burgeoning field of Artificial Intelligence (AI) continues to permeate fabric healthcare, particularly in realms patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors unravel intricacies recent AI advancements their profound implications for reconceptualizing delivery medical care. Through introduction innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, automated appointment systems, is not only amplifying quality care but also empowering patients fostering more interactive dynamic between healthcare provider. Yet, this progressive infiltration into sphere grapples with plethora challenges hitherto unseen. The exigent issues data security privacy, specter algorithmic bias, requisite adaptability regulatory frameworks, matter acceptance trust solutions demand immediate thoughtful resolution .The importance establishing stringent far-reaching policies, ensuring technological impartiality, cultivating confidence paramount ensure that AI-driven enhancements service provision remain both ethically sound efficient. In conclusion, we advocate an expansion research efforts aimed at navigating ethical complexities inherent technology-evolving landscape, catalyzing policy innovation, devising applications are clinically effective earn populace. By melding expertise across disciplines, stand threshold wherein AI's role unimpeachable conducive elevating global health quotient.

Language: Английский

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Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review

Biomedicines, Journal Year: 2023, Volume and Issue: 11(6), P. 1612 - 1612

Published: June 1, 2023

Oral cancer (OC) is one of the most common forms head and neck continues to have lowest survival rates worldwide, even with advancements in research therapy. The prognosis OC has not significantly improved recent years, presenting a persistent challenge biomedical field. In field oncology, artificial intelligence (AI) seen rapid development, notable successes being reported times. This systematic review aimed critically appraise available evidence regarding utilization AI diagnosis, classification, prediction oral using histopathological images. An electronic search several databases, including PubMed, Scopus, Embase, Cochrane Library, Web Science, Google Scholar, Saudi Digital was conducted for articles published between January 2000 2023. Nineteen that met inclusion criteria were then subjected critical analysis utilizing QUADAS-2, certainty assessed GRADE approach. models been widely applied diagnosing cancer, differentiating normal malignant regions, predicting patients, grading OC. used these studies displayed an accuracy range from 89.47% 100%, sensitivity 97.76% 99.26%, specificity ranging 92% 99.42%. models’ abilities diagnose, classify, predict occurrence outperform existing clinical approaches. demonstrates potential deliver superior level precision accuracy, helping pathologists improve their diagnostic outcomes reduce probability errors. Considering advantages, regulatory bodies policymakers should expedite process approval marketing products application scenarios.

Language: Английский

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Exploring a decade of deep learning in dentistry: A comprehensive mapping review

Clinical Oral Investigations, Journal Year: 2025, Volume and Issue: 29(2)

Published: Feb. 19, 2025

Language: Английский

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MSDeepAMR: antimicrobial resistance prediction based on deep neural networks and transfer learning

Frontiers in Microbiology, Journal Year: 2024, Volume and Issue: 15

Published: April 17, 2024

Introduction Antimicrobial resistance (AMR) is a global health problem that requires early and effective treatments to prevent the indiscriminate use of antimicrobial drugs outcome infections. Mass Spectrometry (MS), more particularly MALDI-TOF, have been widely adopted by routine clinical microbiology laboratories identify bacterial species detect AMR. The analysis AMR with deep learning still recent, most models depend on filters preprocessing techniques manually applied spectra. Methods This study propose neural network, MSDeepAMR, learn from raw mass spectra predict MSDeepAMR model was implemented for Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus under different antibiotic profiles. Additionally, transfer test performed benefits adapting previously trained external data. Results showed good classification performance resistance. AUROC above 0.83 in cases studied, improving results previous investigations over 10%. adapted improved up 20% when compared only Discussion demonstrate potential their MS allow extrapolation de used need do not capacity an extensive sample collection.

Language: Английский

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Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions

Current Oncology, Journal Year: 2024, Volume and Issue: 31(9), P. 5255 - 5290

Published: Sept. 6, 2024

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning natural language processing, their applications HNC. The integration of with imaging techniques, genomics, electronic health records explored, emphasizing its role early detection, biomarker discovery, planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, real-time monitoring systems are poised to further advance field. Addressing these fostering among experts, clinicians, researchers crucial developing equitable effective applications. future HNC holds significant promise, offering potential breakthroughs diagnostics, personalized therapies, improved patient outcomes.

Language: Английский

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Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images

Expert Systems, Journal Year: 2024, Volume and Issue: 42(1)

Published: Jan. 9, 2024

Abstract The most prevalent type of cancer worldwide is mouth cancer. Around 2.5% deaths are reported annually due to oral in 2023. Early diagnosis squamous cell carcinoma (OSCC), a cavity cancer, essential for treating and recovering patients. A few computerized techniques exist but focused on traditional machine learning methods, such as handcrafted features. In this work, we proposed fully automated architecture based Self‐Attention convolutional neural network Residual Network information fusion optimization. the framework, augmentation process performed training testing samples, then two developed deep models trained. self‐attention MobileNet‐V2 model trained using an augmented dataset. parallel, DarkNet‐19 same dataset, whereas hyperparameters have been initialized whale optimization algorithm (WOA). Features extracted from deeper layers both fused canonical correlation analysis (CCA) approach. CCA approach further optimized improved WOA version named Quantum that removes irrelevant features selects only important ones. final selected classified networks wide networks. experimental dataset includes sets: 100× 400×. Using sets, method obtained accuracy 98.7% 96.3%. Comparison conducted with state‐of‐the‐art (SOTA) shows significant improvement precision rate.

Language: Английский

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Investigation of the effects of porosity and volume fraction on the atomic behavior of cancer cells and microvascular cells of 3DN5 and 5OTF macromolecular structures during hematogenous metastasis using the molecular dynamics method

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 158, P. 106832 - 106832

Published: April 5, 2023

Language: Английский

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An Efficient Technique for the Better Recognition of Oral Cancer using Support Vector Machine

2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS), Journal Year: 2023, Volume and Issue: unknown, P. 1252 - 1257

Published: May 17, 2023

Accuracy is among the most important factors in a disease diagnosis. It essential to select characteristics that you find pertinent for highest accuracy. This study aims more accurately predict presence of primary stage squamous cell carcinoma using fewer indicators. Stages oral cancer were first demonstrated be predicted by 25 features. The variety features are obtained from various patient records indirectly decreased this through combination unified medical system hybrid selection techniques identify useful identification cancer. Hybrid feature has been used condense qualities into 14 diagnosis patients with then four classifiers: Updatable Naive Bayes, Multilayer Perceptrons, K-Nearest Neighbor, and Support Vector Machines. Also, data show that, after adding development decisions SMOTE during preprocessing phases, support vector machine's performance surpasses other machine learning techniques.

Language: Английский

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Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges

Pharmaceutics, Journal Year: 2024, Volume and Issue: 16(2), P. 260 - 260

Published: Feb. 9, 2024

The use of data-driven high-throughput analytical techniques, which has given rise to computational oncology, is undisputed. widespread machine learning (ML) and mathematical modeling (MM)-based techniques widely acknowledged. These two approaches have fueled the advancement in cancer research eventually led uptake telemedicine care. For diagnostic, prognostic, treatment purposes concerning different types research, vast databases varied information with manifold dimensions are required, indeed, all this can only be managed by an automated system developed utilizing ML MM. In addition, MM being used probe relationship between pharmacokinetics pharmacodynamics (PK/PD interactions) anti-cancer substances improve treatment, also refine quality existing models incorporated at steps development related routine patient This review will serve as a consolidation benefits special focus on area prognosis anticancer therapy, leading identification challenges (data quantity, ethical consideration, data privacy) yet fully addressed current studies.

Language: Английский

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