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

Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review

BACKGROUND

Cancer remains a significant health challenge in the ASEAN region, highlighting the need for effective screening programs. However, approaches, target demographics, and intervals vary across ASEAN member states, necessitating a comprehensive understanding of these variations to assess program effectiveness. Additionally, while artificial intelligence (AI) holds promise as a tool for cancer screening, its utilization in the ASEAN region is unexplored.

PURPOSE

This study aims to identify and evaluate different cancer screening programs across ASEAN, with a focus on assessing the integration and impact of AI in these programs.

METHODS

A scoping review was conducted using PRISMA-ScR guidelines to provide a comprehensive overview of cancer screening programs and AI usage across ASEAN. Data were collected from government health ministries, official guidelines, literature databases, and relevant documents. The use of AI in cancer screening reviews involved searches through PubMed, Scopus, and Google Scholar with the inclusion criteria of only included studies that utilized data from the ASEAN region from January 2019 to May 2024.

RESULTS

The findings reveal diverse cancer screening approaches in ASEAN. Countries like Myanmar, Laos, Cambodia, Vietnam, Brunei, Philippines, Indonesia and Timor-Leste primarily adopt opportunistic screening, while Singapore, Malaysia, and Thailand focus on organized programs. Cervical cancer screening is widespread, using both opportunistic and organized methods. Fourteen studies were included in the scoping review, covering breast (5 studies), cervical (2 studies), colon (4 studies), hepatic (1 study), lung (1 study), and oral (1 study) cancers. Studies revealed that different stages of AI integration for cancer screening: prospective clinical evaluation (50%), silent trial (36%) and exploratory model development (14%), with promising results in enhancing cancer screening accuracy and efficiency.

CONCLUSION

Cancer screening programs in the ASEAN region require more organized approaches targeting appropriate age groups at regular intervals to meet the WHO's 2030 screening targets. Efforts to integrate AI in Singapore, Malaysia, Vietnam, Thailand, and Indonesia show promise in optimizing screening processes, reducing costs, and improving early detection. AI technology integration enhances cancer identification accuracy during screening, improving early detection and cancer management across the ASEAN region.

Results comparison of cervical cancer early detection using cerviray ® with VIA test

OBJECTIVES

This study investigates the performance of artificial intelligence (AI) technology, namely Cerviray AI®, compared with Cerviray® expert, aiming to compare its sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC ROC). The Visual Inspection with Acetic Acid (VIA) test is used as the gold standard.

RESULTS

The study involved 44 patients from various health centers in West Java Province. Performance of Cerviray AI®, or Cerviray® expert, and lastly VIA tests were compared in their ability to detect pre-cancerous cervical lesions in high-risk women of childbearing age. The current study indicated that Cerviray AI® had a sensitivity of 42.9%, specificity of 100%, PPV of 100%, and ROC AUC values of 71.4%. In comparison, the evaluation of the Cerviray® expert demonstrated a sensitivity of 71.4%, specificity of 97.3%, PPV of 83.3%, and ROC AUC values of 84.4%. In conclusion, the evaluation of Cerviray® expert outperformed Cerviray AI® in ROC AUC values.

TRIAL REGISTRATION

Clinical Trials.gov Identifier NCT06518070 Retrospectively registered.

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

BACKGROUND

The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images.

OBJECTIVES

To 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 STRATEGY

Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords.

SELECTION CRITERIA

Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases.

DATA COLLECTION AND ANALYSIS

A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance.

MAIN RESULTS

We 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%.

CONCLUSION

We 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.

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

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.