Explainable ensemble learning model improves identification of candidates for oral cancer screening

Adeoye, John; Zheng, Li‐Wu; Thomson, Peter; Choi, S. W.; Su, Youfeng

doi:10.1016/j.oraloncology.2022.106278

Cited by 9 publications

(2 citation statements)

References 55 publications

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“…Afterward, outperforming model(s) may be selected by comparing the average of the performance measures (such as sensitivity, specificity, precision, negative predictive value, AUC, positive and negative likelihood ratios, and Youden's index) obtained from cross‐validation (Collins et al, 2015; Moons et al, 2019). In the event of multiple models with good accuracy, a correlation analysis of their outputs may be performed to determine whether constructing a voting or stacking ensemble model that incorporates outputs from each of them is warranted (Adeoye et al, 2023; Tang et al, 2022). The final AI‐assisted biomarker‐based model may then be investigated to determine their accuracy, calibration, and net benefit for predicting disease outcomes in independent patient samples before a PRoBE trial is conducted.…”

Section: Implementing Ai‐assisted Saliva Liquid Biopsy For Oral and M...mentioning

confidence: 99%

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Adeoye

2023

Oral Diseases

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Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease‐related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi‐omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

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Section: Implementing Ai‐assisted Saliva Liquid Biopsy For Oral and M...mentioning

confidence: 99%

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Adeoye

2023

Oral Diseases

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“…Furthermore, cervical cancer [66], skin cancer [67,68], oral cancer [69,70], esophageal squamous cell carcinoma and adenocarcinoma of the esophagogastric junction [71] can also be detected and distinguished early using AI models. The above studies greatly demonstrate the potential of AI models in detecting early cancers.…”

Section: Tumor Screening and Early Detectionmentioning

confidence: 99%

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Zhang,

Xu,

Tang

et al. 2023

J Hematol Oncol

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Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.

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Synthesis of clinical images for oral cancer detection and prediction using deep learning

Giri,

Dash

2025

Mining Biomedical Text, Images and Visual Features for Information Retrieval

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Explainable ensemble learning model improves identification of candidates for oral cancer screening

Cited by 9 publications

References 55 publications

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment

Synthesis of clinical images for oral cancer detection and prediction using deep learning

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