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Vaccines need to be continuously stored between 2°C to 8°C, from their production to administration to beneficiaries. Every year, more than 25% of vaccines are wasted. One of the main reasons for this wastage is the lack of cold chain continuity in low-income settings, where electricity is scarce. Recently, several advances have been made in cooling technologies to store and transport vaccines. The current paper presents a review of refrigeration technologies based on scientific publications, industry white papers and other grey literature. For each refrigeration method, we describe its working principle, the best performing devices available as well as the remaining research challenges in order to obtain a very high degree of performance enhancement. Finally, we comment on their applicability for vaccine transport and storage.

Introduction Cervical cancer remains a major public health challenge in low- and middle-income countries (LMICs) due to financial and logistical issues. WHO recommendation for cervical cancer screening in LMICs includes HPV testing as primary screening followed by visual inspection with acetic acid (VIA) and treatment. However, VIA is a subjective procedure dependent on the healthcare provider’s experience. Its accuracy can be improved by computer-aided detection techniques. Our aim is to assess the performance of a smartphone-based Automated VIA Classifier (AVC) relying on Artificial Intelligence to discriminate precancerous and cancerous lesions from normal cervical tissue. Methods The AVC study will be nested in an ongoing cervical cancer screening program called “3T-study” (for Test, Triage and Treat), including HPV self-sampling followed by VIA triage and treatment if needed. After application of acetic acid on the cervix, precancerous and cancerous cells whiten more rapidly than non-cancerous ones and their whiteness persists stronger overtime. The AVC relies on this key feature to determine whether the cervix is suspect for precancer or cancer. In order to train and validate the AVC, 6000 women aged 30 to 49 years meeting the inclusion criteria will be recruited on a voluntary basis, with an estimated 100 CIN2+, calculated using a confidence level of 95% and an estimated sensitivity of 90% +/-7% precision on either side. Diagnostic test performance of AVC test and two current standard tests (VIA and cytology) used routinely for triage will be evaluated and compared. Histopathological examination will serve as reference standard. Participants’ and providers’ acceptability of the technology will also be assessed. The study protocol was registered under ClinicalTrials.gov (number NCT04859530). Expected results The study will determine whether AVC test can be an effective method for cervical cancer screening in LMICs.

Background: The World Health Organization (WHO) recommendations for promoting effective management of cervical cancer screening in low- and medium-income countries (LMIC) include human papillomavirus (HPV) testing as primary screening followed by visual inspection with acetic acid (VIA) and, if required, treatment. The application of acetic acid induces a transient whitening effect which appears and disappears differently in precancerous lesions and cancer than in benign conditions. However, this assessment by human observers is generally subjective and accuracy is limited. This study presents a systematic review of the automated algorithms for cervical (pre)cancer screening based on images taken during VIA with the objective of assessing their potential as screening tool.Methods: We performed a systematic literature search in PubMed, Google Scholar and Scopus. The selected studies introduce automated algorithms for the classification of cervical intraepithelial neoplasia grade 2 or higher (CIN2+) with respect to benign conditions, based only on images taken during VIA. We included studies that use, as gold standard, histopathology for CIN2+ cases and, histopathology or normal cytology and colposcopy for benign conditions. The selected studies were analysed in terms of specificity and sensitivity. From each study, the algorithm with the highest accuracy was further studied considering key features such as type of algorithms, acquisition devices, the number of images used per patient, or its performance in comparison to the experts’ classification. The quality and risk of the studies was assessed following the QUADAS-2 guidelines.Results: Of the 1519 studies identified, nine met the inclusion criteria. The algorithms with the highest accuracy from each study reported a sensitivity and specificity values ranging from 0.60 to 0.93 and 0.67 to 0.95, respectively. Conclusion: Machine learning-based cervical cancer screening algorithms have the potential to become a key tool for cervical cancer screening in countries that suffer from a lack of healthcare infrastructure and personnel. Nevertheless, the selected studies assess their algorithms using small datasets made of highly selected images without reflecting real screened populations. Large-scale and real conditions testing is required to assess the potential of these algorithms as the future of cervical cancer screening.Systematic review registration: PROSPERO CRD42021270745