Automated analysis of digital medical images in cervical cancer screening: A systematic review - Ledwaba, Leshego (Department of Human Biology, 2024)
Background Cervical cancer is the second highest cause of mortalities in women living in resource-constrained countries compared to those living in high income countries, due to lack of organized population screening. Cervical cancer screening is the best way to detect lesions and remove them before they advance into malignancy. In South Africa, the current standard cervical cancer screening protocol begins with cytology examination then a referral for colposcopy follows if the cytology screening test is abnormal. Thereafter, histopathology examination is conducted on biopsy specimen collected during colposcopy. Biopsy specimen are only collected when suspicious lesions are observed during colposcopy. These screening procedures need to be performed by qualified specialist clinicians because accuracy of diagnosis is highly dependent on the skill level and experience of the clinician making the diagnostic decision. In South Africa, colposcopy and cytology are constrained by a shortage of specialists and expensive diagnostic infrastructure. Consequently, public health interventions such as population screening programs for cervical cancer are poorly implemented. Researchers have been developing low-cost portable devices, some of which are incorporated with automated image analysis to enhance diagnostic decision-making. The methods for incorporating automation within each domain of the cervical cancer screening protocol are becoming numerous as researchers continue to advance the field. As the knowledge base is growing rapidly, progress on the implementation status of novel imaging devices and novel algorithms in cervical cancer screening has become unclear. Thus, there is a need to identify all relevant technologies, i.e. devices and algorithms, currently being researched in the field, and to understand their unique strengths and challenges toward clinical adoption. The aim of this project was to provide a systematic review summarizing the full range of automated technology systems used in cervical cancer screening. Method A systematic search on five main academic databases (PubMed, Scopus, EBSCOhost, Web of Science and Google Scholar) was conducted to identify articles on automated technology systems applied in cervical cancer screening. The search results were screened by two independent reviewers to assess eligibility in meeting Population, Intervention, Comparator, and Outcome (PICO) criteria. The screening of articles was a two-step approach: firstly, screening for eligibility by only reading the title and abstract of articles; then secondly, screening by reading full texts. A data extraction form was developed and used to systematically summarize information contained in 70 studies that were included for analysis. Bias in each study was assessed using a risk of bias template adapted from established checklists, namely the Cincinnati Children's LEGEND guideline and the Joanna Briggs Institute critical appraisal checklist for diagnostic test accuracy studies. A conceptual map of common computer aided diagnostics (CAD) tasks that make up the automation pipeline was developed as a narrative tool to synthesize the specific functions that proposed CAD algorithms in multiple screening domains were performing. Results This systematic review found 16 studies which reported application of algorithms paired with novel image acquisition devices, and 52 studies reporting on standalone image analysis algorithms. CAD algorithms associated with acquisition devices (both novel and conventional) revealed that automated analysis achieved superior performance than manual expert analysis; thus, improving diagnostic decisions made by clinicians performing colposcopy, cytology and histopathology. The pertinent algorithms were those developed for devices designed with a mobile phone or tablet, which were the Pocket Colposcope, MobileODT EVA Colpo, Smartphone Camera, Smartphone-based Endoscope System, Smartscope, Mobile high resolution micro-endoscopy (mHRME), and Pi high resolution micro-endoscopy (PiHRME). These mobile-based systems in particular could be applied more widely in low- to middle-income countries than bulky devices because of their anticipated low purchase cost. Most interventions were in the feasibility stage of development, undergoing initial clinical validations. Conclusion This review found that cervical cancer screening researchers have proven the positive clinical impact that CAD algorithms might have in reaching outstanding prediction performance. This accomplishment is a significant step toward minimizing reliance on experts to provide cervical cancer screening services. Furthermore, the systematic review summarized evidence of the algorithms which are being created utilizing portable devices, to circumvent constraints prohibiting wider implementation in LMICs (such as expensive diagnostic infrastructure). These advances can make the decentralization of colposcopy services more feasible if unsupervised community health workers are trained to effectively utilize portable imaging devices with automated functionality for interpreting results. However clinical validation of promising novel systems is not yet implemented adequately in LMICs, because most studies did not include nurses who are a crucial segment of the target population. Additionally, it is not clear whether the proposed portable interventions are economically feasible when hidden costs are also taken into account.