Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review
Leila Allahqoli,Antonio Simone Laganà,Afrooz Mazidimoradi,Hamid Salehiniya,Veronika Günther,Vito Chiantera,Shirin Karimi Karimi Goghari,Mohammad Matin Ghiasvand,Azam Rahmani,Zohre Momenimovahed,Ibrahim Alkatout +10 more
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TLDR
A systematic review as mentioned in this paper evaluated the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions, and found that the accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%.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 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.read more
Citations
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A research study on the cervical cerclage to deal with cervical insufficiency using machine learning
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Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges
TL;DR: A review of state-of-the-art approaches that use deep learning techniques to analyze cervical cytology and screening images is presented in this paper , where the authors discuss relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytologies and colposcopy images.
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Learning Laparoscopic Radical Hysterectomy: Are We Facing an Emerging Situation?
Graziella Moufawad,Antonio Simone Laganà,Nassir Habib,Vito Chiantera,Andrea Giannini,Federico Ferrari,Amerigo Vitagliano,Luigi Della Corte,Giuseppe Bifulco,Zaki Sleiman +9 more
TL;DR: In this article , the learning curve of radical hysterectomy, especially by laparoscopy, is influenced by several factors, such as the LACC trial, the decrease in cervical cancer incidence, and the use of robotic-assisted techniques.
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Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges
TL;DR: A review of state-of-the-art approaches that use deep learning techniques to analyze cervical cytology and screening images is presented in this paper , where the authors discuss relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytologies and colposcopy images.
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The trend of change in cervical tumor size and time to death of hospitalized patients in northwestern Ethiopia during 2018–2022: Retrospective study design
TL;DR: In this paper , the authors determined factors that affect the longitudinal change of tumor size and the time to death of outpatients with Cervical cancer, which is the fourth most common cause of cancer-related death in the world.
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