Computational Intelligence in Multi-Omics Aiding Early Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 7053

Special Issue Editor


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Guest Editor
Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore, Pakistan
Interests: computer aided diagnosis; image processing; neural commputing
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Special Issue Information

Dear Colleagues,

As the secrets of the medical and biological information related to diseases are gradually unlocked, scientists and researchers have observed that nearly all diseases have several factors that can aid in their early diagnosis. The identification of such factors is an important step for early diagnosis; however, using biological and clinical approaches for this purpose can be resource-demanding, time-consuming and costly. Thus, prior to such measures, one could opt for computational-intelligence-based approaches to produce results efficiently and accurately. This can be achieved through the analysis of multiomics data (involving genomics, proteomics, transcriptomics, epigenomics, etc.) as well as clinical profiles, biomedical records and biomedical images. This Special Issue in Diagnostics aims to publish original research and review articles exploring the role of computational intelligence in multiomics to aid early diagnosis of diseases. The scope of this Issue mainly covers the use of computational intelligence approaches, such as machine learning and deep learning, for the analysis of medical, biological and multiomics data of all natures and associated with any disease.

Topics of interest include, but are not limited to:

  • Novel algorithms for advancement in early diagnosis using biological, medical and omics data;
  • The use of computational intelligence in diagnostic biomarkers, with a focus on early diagnosis;
  • Early diagnosis of diseases using medical image analysis;
  • Computational intelligence in bioinformatics-aided early diagnosis;
  • Biomedical applications related to early diagnosis.

Prof. Dr. Yaser Daanial Khan 
Guest Editor

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Keywords

  • computational intelligence
  • early diagnosis
  • multiomics
  • genomics
  • proteomics
  • transcriptomics
  • medical images
  • biomedical data
  • classification
  • detection
  • machine learning
  • deep learning

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Published Papers (1 paper)

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32 pages, 787 KiB  
Systematic Review
Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review
by Leila Allahqoli, Antonio Simone Laganà, Afrooz Mazidimoradi, Hamid Salehiniya, Veronika Günther, Vito Chiantera, Shirin Karimi Goghari, Mohammad Matin Ghiasvand, Azam Rahmani, Zohre Momenimovahed and Ibrahim Alkatout
Diagnostics 2022, 12(11), 2771; https://doi.org/10.3390/diagnostics12112771 - 13 Nov 2022
Cited by 34 | Viewed by 6425
Abstract
Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of [...] Read more.
Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images. Full article
(This article belongs to the Special Issue Computational Intelligence in Multi-Omics Aiding Early Diagnosis)
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