Table 2 Characteristics of the reviewed studies focusing on the utilization of AI technology in the ASEAN region until May 2024
From: Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review
Author (Year) | Country Data Used | Type of Cancer | Study Title | Study Design | Type of AI method | AI Integration Stage (40,41) | ||
|---|---|---|---|---|---|---|---|---|
Sachithanandan et al. (2024) [82] | Malaysia | Lung | The potential role of artificial intelligence-assisted chest X-ray imaging in detecting early-stage lung cancer in the community—a proposed algorithm for lung cancer screening in Malaysia | Developed algorithm for lung cancer screening in Malaysia | Machine learning (ML) and Deep learning (DL) assisted chest radiography | Exploratory model development | ||
Pham et al. (2023) [83] | Vietnam | Breast | Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer | Multimodal appraoch | A multi-featured machine learning model(ML) combining genome-wide methylation change | Silent trial | ||
Le et al. (2024) [84] | Vietnam | Breast | Transfer learning for deep neural networks-based classification of breast cancer X-ray images | Application of transfer learning technique | Deep convolutional neural networks (CNNs) - (DL), specifically the Residual Network (ResNet 34) model | Prospective Clinical Evaluation | ||
Nabheerong et al. (2023) [85] | Thailand | Breast | Breast Cancer Screening Using a Modified Inertial Projective Algorithms for Split Feasibility Problems | Algorithm Developed | The inertial relaxed CQ algorithm | Exploratory model development | ||
Hanis et al. (2022) [86] | Malaysia | Breast | Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records | Retrospective study using data collected from patient registration records. | Machine learning (ML) algorithm | Silent trial | ||
Hamid et al. (2024) [87] | Malaysia | Breast | Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation | Retrospective cross-sectional study | Deep learning model (DL)- Lunit INSIGHT MMG (version 1.1.7.2, Lunit, South Korea) | Silent trial | ||
Mohamad Marzuki et al. (2019) [88] | Malaysia | Colon | Usable Mobile App for Community Education on Colorectal Cancer: Development Process and Usability Study | Nominal group technique (NGT) | AI integrated Mobile App | Prospective Clinical Evaluation | ||
Koh et al. (2023) [89] | Singapore | Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore | Prospective cohort study | Deep learning model (DL) -Real-time Computer-Aided Detection (CADe) of polyp’s program using the GI Genius™ Intelligent Endoscopy Module | Prospective Clinical Evaluation | |||
Chin et al. (2023) [90] | Singapore | One-year review of real-time artificial intelligence (AI)-aided endoscopy performance | Prospective cohort study | GI Genius™ Intelligent Endoscopy based on deep learning - convolutional neural networks (CNNs) | Prospective Clinical Evaluation | |||
Lim et al. (2024) [91] | Singapore | ChatGPT on guidelines: Providing contextual knowledge to GPT allows it to provide advice on appropriate colonoscopy intervals | Comparative study using hypothetical patient scenarios | GPT-4 (LLM - Large language model) with contextual knowledge | Silent trial | |||
Harsono et al. (2022) [92] | Indonesia | Cervical | Cervical pre-cancerous lesion detection: development of smartphone-based VIA application using artificial intelligence | Developing an AI-based application | Machine learning model (ML)_Gaussian Mixture Model (GMM) | Prospective Clinical Evaluation | ||
Nurmaini et al.(2023) [93] | Indonesia | Cervical | Real time mobile AI-assisted cervicography interpretation system | Development and evaluation of AI based application | Deep learning model (DL)- Convolutional neural networks (CNNs) using Lightweight You Only Look Once (YOLO) framework | Prospective Clinical Evaluation | ||
Tiyarattanachai et al. (2023) [94] | Thailand | Hepatic | Artificial intelligence assists operators in real-time detection of focal liver lesions during ultrasound: A randomized controlled study | Prospective randomized controlled study. | AI system to assist operators in real-time detection of FLLs during ultrasound examinations | Prospective Clinical Evaluation | ||
Warin et al. (2021) [95] | Thailand | Oral | Automatic classification and detection of oral cancer in photographic images using deep learning algorithms | Retrospective analysis using deep learning algorithms. | Deep learning model (DL)- Convolutional Neural Networks (CNNs), specifically DenseNet121 for classification and Faster R-CNN for detection. | Silent trial | ||