Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence

Harsono, Ali Budi; Susiarno, Hadi; Suardi, Dodi; Owen, Louis; Fauzi, Hilman; Kireina, Jessica; Wahid, Rizki Amalia; Carolina, Johanna Sharon; Mantilidewi, Kemala Isnainiasih; Hidayat, Yudi Mulyana

doi:10.1186/s13104-022-06250-6

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…As expected, combining algorithms led to better overall performance. For instance, ResNet+clinical features, 27 ensemble classifier, 24,25 and Cervical Net + SVM 21 achieved remarkable metrics.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

Vargas‐Cardona,

Rodriguez‐Lopez,

Arrivillaga

et al. 2023

Intl J Gynecology & Obste

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BackgroundThe intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.ObjectivesTo describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses Extension for Scoping Reviews (PRISMA‐ScR).Search StrategyArksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.Selection CriteriaIdentified titles and abstracts were screened to select original reports and cross‐checked for overlap of cases.Data Collection and AnalysisA descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.Main ResultsWe identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k‐nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%.ConclusionWe concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

Vargas‐Cardona,

Rodriguez‐Lopez,

Arrivillaga

et al. 2023

Intl J Gynecology & Obste

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“… 5 Lesion characterization via endoscopic or direct visualization in particular is among the most recently studied topics. 6 , 7 This class of applications has the ability to detect subtle changes that could elude the evaluating physician and can note patterns that may or may not be visible to the eye. 8 Radiomic studies that can predict molecular diagnoses or possible behaviors of a lesion are especially benefiting from this technology.…”

Section: Discussionmentioning

confidence: 99%

The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case

Sozer,

Borcek,

Sagiroglu

et al. 2023

Journal of Neurosurgery: Case Lessons

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BACKGROUND The aim of this paper is to report one of the significant applications of artificial intelligence (AI) and how it affects everyday clinical practice in neurosurgery. The authors present a case in which a patient was diagnosed via an AI algorithm during ongoing magnetic resonance imaging (MRI). According to this algorithm, the corresponding physicians were immediately warned, and the patient received prompt appropriate treatment. OBSERVATIONS A 46-year-old female presenting with nonspecific headache was admitted to undergo MRI. Scanning revealed an intraparenchymal mass that was detected by an AI algorithm running on real-time patient data while the patient was still in the MRI scanner. The day after MRI, a stereotactic biopsy was performed. The pathology report confirmed an isocitrate dehydrogenase wild-type diffuse glioma. The patient was referred to the oncology department for evaluation and immediate treatment. LESSONS This is the first report of a glioma diagnosed by an AI algorithm and a subsequent prompt operation in the literature—the first of many and an example of how AI will enhance clinical practice.

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“…For instance, AI algorithms have been deployed to analyze retinal images for diabetic retinopathy screening in Ghana and Kenya, enabling earlier detection and intervention [10]. Another example is the use of AI algorithms in cervical cancer screening using visual classification of low-cost smartphone images [11]. These case studies highlight the potential of AI-driven diagnostic tools to address healthcare disparities and improve health outcomes in African communities.…”

Section: Real-world Examples Of Ai-driven Diagnostic Tool Adoption In...mentioning

confidence: 99%

Transforming African Healthcare with AI: Paving the Way for Improved Health Outcomes

ANDIGEMA,

Tania Cyrielle,

Lethicia Danaëlle

et al. 2024

Preprint

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The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

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Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence

Cited by 8 publications

References 17 publications

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022

The first case of glioma detected by an artificial intelligence algorithm running on real-time data in neurosurgery: illustrative case

Transforming African Healthcare with AI: Paving the Way for Improved Health Outcomes

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