Improving the Pap test with artificial intelligence

An Overview of Artificial Intelligence in Gynaecological Pathology Diagnostics

Background: The advent of artificial intelligence (AI) has revolutionised many fields in healthcare. More recently, it has garnered interest in terms of its potential applications in histopathology, where algorithms are increasingly being explored as adjunct technologies that can support pathologists in diagnosis, molecular typing and prognostication. While many research endeavours have focused on solid tumours, gynaecological malignancies have nevertheless been relatively overlooked. The aim of this review was therefore to provide a summary of the status quo in the field of AI in gynaecological pathology by encompassing malignancies throughout the entirety of the female reproductive tract rather than focusing on individual cancers. Methods: This narrative/scoping review explores the potential application of AI in whole slide image analysis in gynaecological histopathology, drawing on both findings from the research setting (where such technologies largely remain confined), and highlights any findings and/or applications identified and developed in other cancers that could be translated to this arena. Results: A particular focus is given to ovarian, endometrial, cervical and vulval/vaginal tumours. This review discusses different algorithms, their performance and potential applications. Conclusions: The effective application of AI tools is only possible through multidisciplinary co-operation and training.

The Evolving Landscape of Cervical Cancer: Breakthroughs in Screening and Therapy Through Integrating Biotechnology and Artificial Intelligence

Cervical cancer (CC) continues to be a major worldwide health concern, profoundly impacting the lives of countless females worldwide. In low- and middle-income countries (LMICs), where CC prevalence is high, innovative, and cost-effective approaches for prevention, diagnosis, and treatment are vital. These approaches must ensure high response rates with minimal side effects to improve outcomes. The study aims to compile the latest developments in the field of CC, providing insights into the promising future of CC management along with the research gaps and challenges. Integrating biotechnology and artificial intelligence (AI) holds immense potential to revolutionize CC care, from MobileODT screening to precision medicine and innovative therapies. AI enhances healthcare accuracy and improves patient outcomes, especially in CC screening, where its use has increased over the years, showing promising results. Also, combining newly developed strategies with conventional treatment options presents an optimal approach to address the limitations associated with conventional methods. However, further clinical studies are essential for practically implementing these advancements in society. By leveraging these cutting-edge technologies and approaches, there is a substantial opportunity to reduce the global burden of this preventable malignancy, ultimately improving the lives of women in LMICs and beyond.

Analysis of the sensitivity of high-grade squamous intraepithelial lesion Pap diagnosis and interobserver variability with the Hologic Genius Digital Diagnostics System

BACKGROUND

Artificial intelligence (AI)-based systems are transforming cytopathology practice. The aim of this study was to evaluate the sensitivity of high-grade squamous intraepithelial lesion (HSIL) Papanicolaou (Pap) diagnosis assisted by the Hologic Genius Digital Diagnostics System (GDDS).

METHODS

A validation study was performed with 890 ThinPrep Pap tests with the GDDS independently. From this set, a subset of 183 cases originally interpreted as HSIL confirmed histologically were included in this study. The sensitivity for detecting HSIL by three cytopathologists was calculated.

RESULTS

Most HSIL cases were classified as atypical glandular cell/atypical squamous cell-high grade not excluded (AGC/ASC-H) and above by all cytopathologists. Of these cases, 11.5% were classified as low-grade squamous intraepithelial lesion (LSIL) by pathologist A (P-A), 6% by pathologist B (P-B), and 5.5% by pathologist C (P-C); 3.8%, 2.7%, and 1.6% of these cases were classified as atypical squamous cell of unknown significance (ASC-US) by P-A, P-B, and P-C, respectively. The sensitivity for detection of cervical intraepithelial neoplasia 2 and above (CIN2+) lesions was 100% if ASC-US and above (ASC-US+) abnormalities were counted among all three pathologists. The sensitivity for detection of CIN2+ lesions was 84.7%, 91.3%, and 92.9% by P-A, P-B, and P-C, respectively, for ASC-H and above abnormalities. The Kendall W coefficient was 0.722, which indicated strong agreement between all pathologists.

CONCLUSIONS

New-generation AI-assisted Pap test screening systems such as the GDDS have the potential to transform cytology practice. In this study, the GDDS aided in interpreting HSIL in ThinPrep Pap tests, with good sensitivity and agreement between the pathologists who interacted with this system.

[What contribution can make artificial intelligence to urinary cytology?]

Urinary cytology using the Paris system is still the method of choice for screening high-grade urothelial carcinomas. However, the use of the objective criteria described in this terminology shows a lack of inter- and intra-observer reproducibility. Moreover, if its sensitivity is excellent on instrumented urine, it remains insufficient on voided urine samples. Urinary cytology appears to be an excellent model for the application of artificial intelligence to improve performance, since the objective criteria of the Paris system are defined at cellular level, and the resulting diagnostic approach is presented in a highly "algorithmic" way. Nevertheless, there is no commercially available morphological diagnostic aid, and very few predictive devices are still undergoing clinical validation. The analysis of different systems using artificial intelligence in urinary cytology rises clear prospects for mutual contributions.

Evolution of Pathological Techniques for the Screening of Cervical Cancer: A Comprehensive Review

The evolutionary journey of cervical cancer screening has been a major medical success story, considering the substantial role it has played in dwindling the disease burden. Through sustained collaborative efforts within the medical community, significant advances have been made from the humble yet path-breaking conventional Pap smear to the current automated screening systems and human papillomavirus (HPV) molecular testing. With the integration of artificial intelligence into screening techniques, we are currently at the precipice of circumventing the pitfalls of manual cytology readings and improving the efficiency of the screening systems by a significant margin. Despite the technological milestones traversed, the high logistics and operational cost, besides the technical know-how of operating the automated systems, can pose a major practical challenge in the widespread adoption of these advanced techniques in cervical cancer screening programs. This would suggest the need to adopt strategies that are tailored to the demands and needs of the different settings keeping their limitations in mind. This review aims to take the reader through the entire evolutionary journey of cervical cancer screening programs, highlight the individual merits and demerits of each technique, and discuss the recommendations from the major global guidelines.

The evolution of cervical cancer screening

There are few medical success stories in history as significant as the reduction in cervical cancer incidence. Through the collaborative efforts of dedicated scientific pioneers, the past century has witnessed remarkable advancement that began with the detection of exfoliated cancer cells through cytologic examination to widespread implementation of cervical cancer screening programs to the discovery of the link between cervical cancer and human papillomavirus (HPV). Current screening methods apply HPV-based testing, and artificial intelligence-based screening systems utilizing digitalized cytology images are being used in a continuous effort to optimize the accuracy and efficiency of the Papanicolaou test. This review summarizes the major milestones in cervical cancer screening history to emphasize its evolution as the World Health Organization aims for the global elimination of cervical cancer.

Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X

BACKGROUND

Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting.

METHODS

In this study, the authors report on the development and large-scale validation of a deep-learning tool, AutoParis-X, which can facilitate rapid, semiautonomous examination of urine cytology specimens.

RESULTS

The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters.

CONCLUSIONS

The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials.

Code-free machine learning for classification of central nervous system histopathology images

Machine learning (ML), an application of artificial intelligence, is currently transforming the analysis of biomedical data and specifically of biomedical images including histopathology. The promises of this technology contrast, however, with its currently limited application in routine clinical practice. This discrepancy is in part due to the extent of informatics expertise typically required for implementation of ML. Therefore, we assessed the suitability of 2 publicly accessible code-free ML platforms (Microsoft Custom Vision and Google AutoML), for classification of histopathological images of diagnostic central nervous system tissue samples. When trained with typically 100 to more than 1000 images, both systems were able to perform nontrivial classifications (glioma vs brain metastasis; astrocytoma vs astrocytosis, prediction of 1p/19q co-deletion in IDH-mutant tumors) based on hematoxylin and eosin-stained images with high accuracy (from ∼80% to nearly 100%). External validation of the predicted accuracy and negative control experiments were found to be crucial for verification of the accuracy predicted by the algorithms. Furthermore, we propose a possible diagnostic workflow for pathologists to implement classification of histopathological images based on code-free machine platforms.

Construction of artificial intelligence-assisted English learning resource query system

English has become an important tool for China's opening to the outside world and exchanges with other countries. More and more people have the motivation and requirements to learn English, but under the traditional English learning mode and traditional teaching mode, the cultivation of learners' autonomous learning habits is ignored. This article aims to study the construction of artificial intelligence-assisted English learning resource query system and establish the relevant feedback mechanism of retrieval. This article applies this mechanism to the retrieval of learning resources, so as to provide learners with the learning resources they really need and improve learners' learning efficiency. This article proposes to find the relevant knowledge points by extracting the knowledge points of the retrieval content. It realizes the query expansion based on knowledge and then realizes the expansion of retrieval results. It realizes the mapping of knowledge points on the retrieval content, the query and expansion of knowledge points, and the presentation of learning resources of the knowledge point index. It also uses the relevant feedback mechanism to adjust the retrieval results to meet the retrieval needs of learners. The experimental results show that the number of knowledge points can be increased to 2–4 times by query expansion based on English resources. Thus, the number of learning resources of search results can be increased to 3–10 times, the expansion of search results can be realized, and the overall recall will be greatly improved. In this article, the related methods of artificial intelligence are applied to the construction experiment of the English learning resource query system, which has a certain promotion effect on the construction of the system.