Abstract
Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas. To determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests. In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya. Detection of squamous cell atypia in the digital samples by analysis with the DLS. The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images. Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 × 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (κ = 0.72) and fair compared with the assessment of glass slides (κ = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS. In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.
Highlights
Inadequate access to microscopy diagnostics is a problem in limited-resource areas and impairs the diagnosis of common and treatable conditions.[1]
In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis
Meaning The results of this study suggest that advanced digital microscopy diagnostics, supported by artificial intelligence, are feasible to use in rural, resource-limited settings for detection of abnormal cells in Papanicolaou tests
Summary
Inadequate access to microscopy diagnostics is a problem in limited-resource areas and impairs the diagnosis of common and treatable conditions.[1]. Cervical cancer remains a common and deadly cancer in areas without screening programs.[3] During the decade, the disease incidence is expected to increase, and the yearly mortality is expected to double, with the largest burden of disease occurring in sub-Saharan Africa.[4] vaccinations against human papillomavirus (HPV)[5] have the potential to significantly reduce the disease incidence, but given that the full benefits of even the most efficient vaccination programs will take decades to be fully realized, millions of women remain at risk.[6] screening tests remain essential,[7] and innovative POC diagnostic solutions are needed.[8] Conventional cytology screening (Papanicolaou test analysis) can drastically reduce the incidence and mortality of cervical cancer, but the manual analysis of samples is labor intensive,[9] is prone to variations in sensitivity and reproducibility, and requires medical experts to analyze the samples[10,11]; this makes the process difficult to implement in resource-limited settings.[12] Human papillomavirus infections, which are the causative agent for cervical cancer, can be detected using polymerase chain reaction assays with high sensitivity and reproducibility. Because most HPV infections are transient, the specificity for precancerous lesions is low.[13,14] In high-resource areas, both molecularand cytology-based screening methods are commonly used and are often combined (ie, cotesting) to improve the diagnostic accuracy.[15,16] Digital methods have been proposed to facilitate the visual analysis of Papanicolaou tests, but the development of fully automated systems has been challenging.[17,18] semiautomated systems for Papanicolaou test screening have been developed,[19] they are limited by the need for bulky, expensive laboratory equipment[20,21,22] and are not suitable for use at the POC or in resource-limited settings
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