Pilot Study of an Open-source Image Analysis Software for Automated Screening of Conventional Cervical Smears

Performance characteristics of an artificial intelligence based on convolutional neural network for screening conventional Papanicolaou-stained cervical smears

Background

The conventional Papanicolaou-stained cervical smear is the most common screening test for cervical cancer. The sensitivity of the test in detecting abnormal cells is 67-75% in various studies. Owing to the volume of smears at cancer screening centres, significant man-hours are expended in the test. We have developed a software program for identification of foci of abnormal cells from conventional smears. We have chosen the convolutional neural network (CNN) model for its efficacy in image classification.

Methods

A total of 1838 microphotographs from cervical smears, containing 1301 'normal' foci and 537 'abnormal' foci were included in the study. The data set was split into training, testing and validation sets. A CNN was developed in the Python programming language. The CNN was trained with the training and testing set. At the end of training, 94.64% accuracy was achieved in the testing set. The CNN was then run on the validation set (441 images).

Results

The CNN showed 94.28% sensitivity, 96.01% specificity, 91.66% positive predictive value and 97.30% negative predictive value. The CNN could recognise normal squamous cells, overlapping cells, neutrophils and debris and classify the focus appropriately. False positives were reported when the CNN failed to recognise overlapping cells (2.7% microphotographs). It could correctly label cell clusters with high nuclear cytoplasmic ratio and hyperchromasia. In 1.8% of microphotographs, a false negative was reported.

Conclusion

The CNN showed 95.46% diagnostic accuracy, suggesting potential use in screening.

An evaluation of the construction of the device along with the software for digital archiving, sending the data, and supporting the diagnosis of cervical cancer

Cervical cancer is still an important cause of mortality among women in a number of countries. There are effective methods of prevention and early diagnosis, but they require well-trained medical professionals including cytologists. Within this project, we built a prototype of a new device together with implemented software using U-NET and CNN architectures of neural networks (ANN), to convert the currently used optical microscopes into fully independent scanning and evaluating systems for cytological samples. To evaluate the specificity and sensitivity of the system, 2058 (2000 normal and 58 abnormal samples) consecutive liquid-based cytology (LBC) samples were analysed. The observed sensitivity and specificity to distinguish normal and abnormal samples was 100%. We observed slight incompatibility in the evaluation of the type of abnormality. The use of ANN is promising for increasing the effectiveness of cervical screening. The low cost of neural network usage further increases the potential areas of application of the presented method. Further refinement of neural networks on a larger sample size is required to evaluate the software.