Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study

Artificial Intelligence Applications in Cytopathology: Current State of the Art

The practice of cytopathology has been significantly refined in recent years, largely through the creation of consensus rule sets for the diagnosis of particular specimens (Bethesda, Milan, Paris, and so forth). In general, these diagnostic systems have focused on reducing intraobserver variance, removing nebulous/redundant categories, reducing the use of "atypical" diagnoses, and promoting the use of quantitative scoring systems while providing a uniform language to communicate these results. Computational pathology is a natural offshoot of this process in that it promises 100% reproducible diagnoses rendered by quantitative processes that are free from many of the biases of human practitioners.

A lightweight xAI approach to cervical cancer classification

Cervical cancer is caused in the vast majority of cases by the human papilloma virus (HPV) through sexual contact and requires a specific molecular-based analysis to be detected. As an HPV vaccine is available, the incidence of cervical cancer is up to ten times higher in areas without adequate healthcare resources. In recent years, liquid cytology has been used to overcome these shortcomings and perform mass screening. In addition, classifiers based on convolutional neural networks can be developed to help pathologists diagnose the disease. However, these systems always require the final verification of a pathologist to make a final diagnosis. For this reason, explainable AI techniques are required to highlight the most significant data to the healthcare professional, as it can be used to determine the confidence in the results and the areas of the image used for classification (allowing the professional to point out the areas he/she thinks are most important and cross-check them against those detected by the system in order to create incremental learning systems). In this work, a 4-phase optimization process is used to obtain a custom deep-learning classifier for distinguishing between 4 severity classes of cervical cancer with liquid-cytology images. The final classifier obtains an accuracy over 97% for 4 classes and 100% for 2 classes with execution times under 1 s (including the final report generation). Compared to previous works, the proposed classifier obtains better accuracy results with a lower computational cost.

Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population

BACKGROUND

Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage rates of human papillomavirus (HPV) vaccines and routine screening are low in most developing countries and even some developed countries. This study aimed to explore the benefit of an artificial intelligence-assisted cytology (AI) system in a screening program for a cervical cancer high-risk population in China.

METHODS

A total of 1231 liquid-based cytology (LBC) slides from women who underwent colposcopy at the Chinese PLA General Hospital from 2018 to 2020 were collected. All women had received a histological diagnosis based on the results of colposcopy and biopsy. The sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), false-positive rate (FPR), false-negative rate (FNR), overall accuracy (OA), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and Youden index (YI) of the AI, LBC, HPV, LBC + HPV, AI + LBC, AI + HPV and HPV Seq LBC screening strategies at low-grade squamous intraepithelial lesion (LSIL) and high-grade squamous intraepithelial lesion (HSIL) thresholds were calculated to assess their effectiveness. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic values of the different screening strategies.

RESULTS

The Se and Sp of the primary AI-alone strategy at the LSIL and HSIL thresholds were superior to those of the LBC + HPV cotesting strategy. Among the screening strategies, the YIs of the AI strategy at the LSIL + threshold and HSIL + threshold were the highest. At the HSIL + threshold, the AI strategy achieved the best result, with an AUC value of 0.621 (95% CI, 0.587-0.654), whereas HPV testing achieved the worst result, with an AUC value of 0.521 (95% CI, 0.484-0.559). Similarly, at the LSIL + threshold, the LBC-based strategy achieved the best result, with an AUC of 0.637 (95% CI, 0.606-0.668), whereas HPV testing achieved the worst result, with an AUC of 0.524 (95% CI, 0.491-0.557). Moreover, the AUCs of the AI and LBC strategies at this threshold were similar (0.631 and 0.637, respectively).

CONCLUSIONS

These results confirmed that AI-only screening was the most authoritative method for diagnosing HSILs and LSILs, improving the accuracy of colposcopy diagnosis, and was more beneficial for patients than traditional LBC + HPV cotesting.

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective prevention. This research aimed to create and validate an artificial intelligence cervical cancer screening (AICCS) system for grading cervical cytology. The AICCS system was trained and validated using various datasets, including retrospective, prospective, and randomized observational trial data, involving a total of 16,056 participants. It utilized two artificial intelligence (AI) models: one for detecting cells at the patch-level and another for classifying whole-slide image (WSIs). The AICCS consistently showed high accuracy in predicting cytology grades across different datasets. In the prospective assessment, it achieved an area under curve (AUC) of 0.947, a sensitivity of 0.946, a specificity of 0.890, and an accuracy of 0.892. Remarkably, the randomized observational trial revealed that the AICCS-assisted cytopathologists had a significantly higher AUC, specificity, and accuracy than cytopathologists alone, with a notable 13.3% enhancement in sensitivity. Thus, AICCS holds promise as an additional tool for accurate and efficient cervical cancer screening.

The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force

INTRODUCTION

The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force.

MATERIALS AND METHODS

A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed.

RESULTS

In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice.

CONCLUSIONS

Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.

Digital cytology part 2: artificial intelligence in cytology: a concept paper with review and recommendations from the American Society of Cytopathology Digital Cytology Task Force

Digital cytology and artificial intelligence (AI) are gaining greater adoption in the cytology laboratory. However, peer-reviewed real-world data and literature are lacking in regard to the current clinical landscape. The American Society of Cytopathology in conjunction with the International Academy of Cytology and the Digital Pathology Association established a special task force comprising 20 members with expertise and/or interest in digital cytology. The aim of the group was to investigate the feasibility of incorporating digital cytology, specifically cytology whole slide scanning and AI applications, into the workflow of the laboratory. In turn, the impact on cytopathologists, cytologists (cytotechnologists), and cytology departments were also assessed. The task force reviewed existing literature on digital cytology, conducted a worldwide survey, and held a virtual roundtable discussion on digital cytology and AI with multiple industry corporate representatives. This white paper, presented in 2 parts, summarizes the current state of digital cytology and AI practice in global cytology practice. Part 1 of the white paper is presented as a separate paper which details a review and best practice recommendations for incorporating digital cytology into practice. Part 2 of the white paper presented here provides a comprehensive review of AI in cytology practice along with best practice recommendations and legal considerations. Additionally, the cytology global survey results highlighting current AI practices by various laboratories, as well as current attitudes, are reported.

Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

A novel in vitro prognostic model of bladder cancer based on urine-derived living tumor cells

Bladder cancer (BLCA) remains a difficult malignancy to manage because of its high recurrence, intense follow-up, and invasive diagnostic and treatment techniques. Immune checkpoint inhibitors (ICIs) have forged a new direction for the treatment of BLCA, but it is currently challenging to predict whether an individual patient will be sensitive to ICIs. We collected 43 urine/tumor samples from BLCA patients for primary bladder cancer cells (BCCs) culturing using our previously reported BCC culture platform. We used flow cytometry (FCM) to measure the expression levels of Programmed Death-Ligand 1 (PD-L1) on BCCs before and after interferon-gamma (IFN-γ) treatment and found that PD-L1 expression and the sensitivities to IFN-γ varied among patients. RNA-sequencing, western blotting, and programmed death-1 (PD-1) binding assays confirmed that the BCC FCM-based PD-L1 detection platform (BC-PD-L1) was reliable and was not hindered by the  glycosylation of PD-L1. In the subsequent retrospective study, we found that IFN-γ-stimulated PD-L1 (sPD-L1) expression on BCCs detected by BC-PD-L1 could predict the prognosis of BLCA patients. Importantly, the prognostic value was similar or even better in urine-derived BC-PD-L1 (UBC-PD-L1). Transcriptome analysis showed that BCCs with high sPD-L1 tended to enrich genes associated with the collagen-containing extracellular matrix, cell-cell adhesion, and positive regulation of the immune system. In addition, the UBC-PD-L1 also exhibited predictive value for ICI response in BLCA patients. In conclusion, as a novel personalized urine-detection method, UBC-PD-L1 may provide a rapid, accurate, and non-invasive tool for monitoring tumor progression, predicting therapeutic responses, and helping improve BLCA clinical treatment in future.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION

Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.

METHODS

A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.

RESULTS

Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.

CONCLUSION

This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.

Pivotal Clinical Study to Evaluate the Efficacy and Safety of Assistive Artificial Intelligence-Based Software for Cervical Cancer Diagnosis

Colposcopy is the gold standard diagnostic tool for identifying cervical lesions. However, the accuracy of colposcopies depends on the proficiency of the colposcopist. Machine learning algorithms using an artificial intelligence (AI) system can quickly process large amounts of data and have been successfully applied in several clinical situations. This study evaluated the feasibility of an AI system as an assistive tool for diagnosing high-grade cervical intraepithelial neoplasia lesions compared to the human interpretation of cervical images. This two-centered, crossover, double-blind, randomized controlled trial included 886 randomly selected images. Four colposcopists (two proficient and two inexperienced) independently evaluated cervical images, once with and the other time without the aid of the Cerviray AI^® system (AIDOT, Seoul, Republic of Korea). The AI aid demonstrated improved areas under the curve on the localization receiver-operating characteristic curve compared with the colposcopy impressions of colposcopists (difference 0.12, 95% confidence interval, 0.10-0.14, p < 0.001). Sensitivity and specificity also improved when using the AI system (89.18% vs. 71.33%; p < 0.001, 96.68% vs. 92.16%; p < 0.001, respectively). Additionally, the classification accuracy rate improved with the aid of AI (86.40% vs. 75.45%; p < 0.001). Overall, the AI system could be used as an assistive diagnostic tool for both proficient and inexperienced colposcopists in cervical cancer screenings to estimate the impression and location of pathologic lesions. Further utilization of this system could help inexperienced colposcopists confirm where to perform a biopsy to diagnose high-grade lesions.

Strategies to eliminate cervical cancer in China

Cervical cancer is a widely distributed disease that is preventable and controllable through early intervention. The World Health Organization has identified three key measures, coverage populations and coverage targets to eliminate cervical cancer. The WHO and several countries have conducted model predictions to determine the optimal strategy and timing of cervical cancer elimination. However, specific implementation strategies need to be developed in the context of local conditions. China has a relatively high disease burden of cervical cancer but a low human papillomavirus vaccination rate and cervical cancer screening population coverage. The purpose of this paper is to review interventions and prediction studies for the elimination of cervical cancer and to analyze the problems, challenges and strategies for the elimination of cervical cancer in China.

Deep Learning-Based Recognition of Cervical Squamous Interepithelial Lesions

Cervical squamous intraepithelial lesions (SILs) are precursor lesions of cervical cancer, and their accurate diagnosis enables patients to be treated before malignancy manifests. However, the identification of SILs is usually laborious and has low diagnostic consistency due to the high similarity of pathological SIL images. Although artificial intelligence (AI), especially deep learning algorithms, has drawn a lot of attention for its good performance in cervical cytology tasks, the use of AI for cervical histology is still in its early stages. The feature extraction, representation capabilities, and use of p16 immunohistochemistry (IHC) among existing models are inadequate. Therefore, in this study, we first designed a squamous epithelium segmentation algorithm and assigned the corresponding labels. Second, p16-positive area of IHC slides were extracted with Whole Image Net (WI-Net), followed by mapping the p16-positive area back to the H&E slides and generating a p16-positive mask for training. Finally, the p16-positive areas were inputted into Swin-B and ResNet-50 to classify the SILs. The dataset comprised 6171 patches from 111 patients; patches from 80% of the 90 patients were used for the training set. The accuracy of the Swin-B method for high-grade squamous intraepithelial lesion (HSIL) that we propose was 0.914 [0.889-0.928]. The ResNet-50 model for HSIL achieved an area under the receiver operating characteristic curve (AUC) of 0.935 [0.921-0.946] at the patch level, and the accuracy, sensitivity, and specificity were 0.845, 0.922, and 0.829, respectively. Therefore, our model can accurately identify HSIL, assisting the pathologist in solving actual diagnostic issues and even directing the follow-up treatment of patients.

Continuous artificial intelligence-assisted DNA aneuploidy cytology for surveilling dysplastic oral leukoplakia treated by photodynamic therapy

Oral leukoplakia (OLK) is one of the most common potentially malignant disorders. High-risk lesions require early intervention before developing into oral cancer. Photodynamic therapy (PDT) is a noninvasive technique for premalignant lesions. Scalpel biopsy remains a reliable method for monitoring the prognosis of OLK, but it is an invasive procedure with poor reproducibility to suspicious lesions. DNA aneuploidy cytology by oral cytobrush has been proposed as a promising objective and noninvasive tool in screening and diagnosing premalignant and malignant lesions. Here, we discussed the significance of artificial intelligence (AI)-assisted DNA aneuploidy cytology by image cytometry (DNA-ICM) for surveilling non-homogeneous OLK with moderate-to-severe dysplasia that was treated by 5-aminolevulinic acid-mediated PDT (ALA-PDT). The present study provides a scheme of the sequential management and surveillance strategy for OLK.

HPV-negative high-grade cervical precancerous lesions or invasive cancer in China: A post hoc analysis of a multicentric clinical study

OBJECTIVE

To evaluate HPV-negative cervical high-grade precancerous lesions or cancer in China.

METHODS

Histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+) were searched in a multicentric clinical study. All patients underwent cobas HPV testing, liquid-based cytology, DNA ploidy analysis, and colposcopy-guided biopsy. According to clinical practice, patients underwent p16^ink4a staining and cone biopsy. Comparisons were made between HPV-negative and -positive patients for clinical characteristics.

RESULTS

The study found 61 cases of cobas HPV-negative CIN2+ among 797 cases of histologically confirmed CIN2+, including 38 CIN2, 20 CIN3, and 3 invasive cancers. The prevalence of HPV-negative CIN2+ and CIN3+ was 7.7% (95% confidence interval [CI] 5.8-9.5) and 5.7% (95% CI 3.5-8.0), respectively. Among 24 cases with p16 staining, 20 showed p16 positivity. The proportions of normal or minor abnormalities in terms of colposcopy, cytology, and DNA ploidy were higher in HPV-negative cases than in HPV-positive cases. When adding cytology to the screening of symptomatic or previously HPV-positive women, the prevalence of HPV-negative CIN2+ or CIN3 would decrease by approximately 50%.

CONCLUSION

Less than one-tenth of CIN2+ are missed by HPV-only screening, and they have smaller lesions than HPV-positive cases. Colposcopy should be considered for symptomatic or previously HPV-positive women with HPV-negative results.

Machine learning-based automated sponge cytology for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction: a nationwide, multicohort, prospective study

BACKGROUND

Oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction have a dismal prognosis, and early detection is key to reduce mortality. However, early detection depends on upper gastrointestinal endoscopy, which is not feasible to implement at a population level. We aimed to develop and validate a fully automated machine learning-based prediction tool integrating a minimally invasive sponge cytology test and epidemiological risk factors for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction before endoscopy.

METHODS

For this multicohort prospective study, we enrolled participants aged 40-75 years undergoing upper gastrointestinal endoscopy screening at 39 tertiary or secondary hospitals in China for model training and testing, and included community-based screening participants for further validation. All participants underwent questionnaire surveys, sponge cytology testing, and endoscopy in a sequential manner. We trained machine learning models to predict a composite outcome of high-grade lesions, defined as histology-confirmed high-grade intraepithelial neoplasia and carcinoma of the oesophagus and oesophagogastric junction. The predictive features included 105 cytological and 15 epidemiological features. Model performance was primarily measured with the area under the receiver operating characteristic curve (AUROC) and average precision. The performance measures for cytologists with AI assistance was also assessed.

FINDINGS

Between Jan 1, 2021, and June 30, 2022, 17 498 eligible participants were involved in model training and validation. In the testing set, the AUROC of the final model was 0·960 (95% CI 0·937 to 0·977) and the average precision was 0·482 (0·470 to 0·494). The model achieved similar performance to consensus of cytologists with AI assistance (AUROC 0·955 [95% CI 0·933 to 0·975]; p=0·749; difference 0·005, 95% CI, -0·011 to 0·020). If the model-defined moderate-risk and high-risk groups were referred for endoscopy, the sensitivity was 94·5% (95% CI 88·8 to 97·5), specificity was 91·9% (91·2 to 92·5), and the predictive positive value was 18·4% (15·6 to 21·6), and 90·3% of endoscopies could be avoided. Further validation in community-based screening showed that the AUROC of the model was 0·964 (95% CI 0·920 to 0·990), and 92·8% of endoscopies could be avoided after risk stratification.

INTERPRETATION

We developed a prediction tool with favourable performance for screening of oesophageal squamous cell carcinoma and adenocarcinoma of the oesophagogastric junction. This approach could prevent the need for endoscopy screening in many low-risk individuals and ensure resource optimisation by prioritising high-risk individuals.

FUNDING

Science and Technology Commission of Shanghai Municipality.

Deep learning for computational cytology: A survey

Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.

Prevalence of High-Risk Human Papillomavirus in Cervical Intraepithelial Neoplasia in the Pre-Vaccine Era - China, 2017-2018

What is already known about this topic?

Human papillomavirus (HPV) type-specific strategies play a key role in the prevention of cervical intraepithelial neoplasia (CIN), but evidence on the HPV type-specific prevalence in CIN is limited.

What is added by this report?

This multicentric study estimates the prevalence of high-risk HPV types in CIN cases and the associations of HPV infection patterns with high-grade CIN in China in 2017-2018.

What are the implications for public health practice?

Population-based prevention strategies should give preference to HPV-16 and 18, and strategies for non-16/18 high-risk HPV are essential for the elimination of cervical cancer.

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

OBJECTIVE

The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions.

MATERIALS AND METHODS

Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.

RESULTS

The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively.

CONCLUSION

The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

Study protocol for artificial intelligence-assisted sponge cytology as pre-endoscopy screening for early esophegeal squmaous epithelial lesions in China

Background

Endoscopic screening is the widely accepted screening strategy for esophageal squmaous cell carcinoma (ESCC). However, massive endoscopic screening is expensive and not cost-efficient, and novel pre-endoscopy detection used as a preliminary screening method arouses new concerns. We are planning to launch an artificial intelligence (AI) assisted sponge cytology for detecting esophageal squmaous high-grade intraepithelial neoplasia (HGIN) and above lesions. The aim of this trail is to investigate the efficiency of AI-assisted sponge cytology in population-based screening of early esophageal squmaous epithelial lesions.

Methods

The study will be prospectively conducted in five regions with a high prevalence of ESCC. AI-assisted sponge cytology and endoscopic examination will be sequentially performed. Based on our previous data, at least 864 patients with esophageal HGIN and above lesions are needed to achieve enough statistical power. And, a calculated 112,500 individuals with high risks of ESCC will be recruited. In the first stage, each 24,000 participants who meet the inclusion criteria will be recruited on a voluntary basis. Setting pathological results as standard reference, diagnostic threshold and according performance of AI-assisted detection will be evaluated. A prediction model will be constructed by co-analyzing cytological results and relevant risk factors. Then, an external validation cohort will be used for validation of the model efficiency. Also, cost-efficiency analysis will be performed. This study protocol was registered on chineseclinicaltrial.gov (ChiCTR1900028524).

Discussion

Our study will determine whether this AI-assisted sponge cytology can be used as an effective pre-endoscopy detection tool for large-scale screening for ESCC in high-risk areas.

Machine learning applications in gynecological cancer: A critical review

Machine Learning (ML) represents a computer science capable of generating predictive models, by exposure to raw, training data, without being rigidly programmed. Over the last few years, ML has gained attention within the field of oncology, with considerable strides in both diagnostic, predictive, and prognostic spectrum of malignancies, but also as a catalyst of cancer research. In this review, we discuss the state of ML applications on gynecologic oncology and systematically address major technical and ethical concerns, with respect to their real-world medical practice translation. Undoubtedly, advances in ML will enable the analysis of large, rather complex, datasets for improved, cost-effective, and efficient clinical decisions.

Effective deep learning for oral exfoliative cytology classification

The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.

Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology

Background

We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy. As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-stained cells in cytopreparation images.

Methods

We evaluated the usefulness of the sliding window method (SWM) and mask region-based convolutional neural network (Mask-RCNN) in identifying the cell nuclei in oral cytopreparation images. Thirty cases of liquid-based oral cytology were analyzed. First, we performed the SWM by dividing each image into 96 × 96 pixels. Overall, 591 images with or without blue-stained cell nuclei were prepared as the training data and 197 as the test data (total: 1,576 images). Next, we performed the Mask-RCNN by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images.

Results

Using the SWM method, the highest detection rate for blue-stained cells in the evaluation group was 0.9314. For Mask-RCNN, 37 cell nuclei were identified, and 1 cell nucleus was identified as a non-nucleus after 40 epochs (error rate:0.027).

Conclusions

Mask-RCNN is more accurate than SWM in identifying the cell nuclei. If the blue-stained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.

Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review

Simple Summary

Artificial intelligence (AI) has attracted significant interest in the healthcare sector due to its promising results. Cytological examination is a critical step in the initial diagnosis of cancer. Here, we conducted a systematic review with quantitative analysis to understand the current status of AI applications in non-gynecological (non-GYN) cancer cytology. In our analysis, we found that most of the studies focused on classification and segmentation tasks. Overall, AI showed promising results for non-GYN cancer cytopathology analysis. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation across all studies.

Abstract

State-of-the-art artificial intelligence (AI) has recently gained considerable interest in the healthcare sector and has provided solutions to problems through automated diagnosis. Cytological examination is a crucial step in the initial diagnosis of cancer, although it shows limited diagnostic efficacy. Recently, AI applications in the processing of cytopathological images have shown promising results despite the elementary level of the technology. Here, we performed a systematic review with a quantitative analysis of recent AI applications in non-gynecological (non-GYN) cancer cytology to understand the current technical status. We searched the major online databases, including MEDLINE, Cochrane Library, and EMBASE, for relevant English articles published from January 2010 to January 2021. The searched query terms were: “artificial intelligence”, “image processing”, “deep learning”, “cytopathology”, and “fine-needle aspiration cytology.” Out of 17,000 studies, only 26 studies (26 models) were included in the full-text review, whereas 13 studies were included for quantitative analysis. There were eight classes of AI models treated of according to target organs: thyroid (n = 11, 39%), urinary bladder (n = 6, 21%), lung (n = 4, 14%), breast (n = 2, 7%), pleural effusion (n = 2, 7%), ovary (n = 1, 4%), pancreas (n = 1, 4%), and prostate (n = 1, 4). Most of the studies focused on classification and segmentation tasks. Although most of the studies showed impressive results, the sizes of the training and validation datasets were limited. Overall, AI is also promising for non-GYN cancer cytopathology analysis, such as pathology or gynecological cytology. However, the lack of well-annotated, large-scale datasets with Z-stacking and external cross-validation was the major limitation found across all studies. Future studies with larger datasets with high-quality annotations and external validation are required.

Artificial intelligence assisted cytological detection for early esophageal squamous epithelial lesions by using low-grade squamous intraepithelial lesion as diagnostic threshold

BACKGROUND

Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high-grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)-assisted cytological diagnosis for such lesions.

METHODS

Low-grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI-assisted diagnosis. The performance of AI-assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large-scale screening was also assessed.

RESULTS

AI-assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10^-76 ) and a better interobserver agreement (93.27% [95% CI, 92.76%-93.74%] vs 65.29% [95% CI, 64.35%-66.22%], p = 1.03 × 10^-84 ). AI-assisted detection showed a higher diagnostic accuracy (96.89% [92.38%-98.57%] vs 72.54% [65.85%-78.35%], p = 1.42 × 10^-14 ), sensitivity (99.35% [95.92%-99.97%] vs 68.39% [60.36%-75.48%], p = 7.11 × 10^-15 ), and negative predictive value (NPV) (97.06% [82.95%-99.85%] vs 40.96% [30.46%-52.31%], p = 1.42 × 10^-14 ). Specificity and positive predictive value (PPV) were not significantly differed. AI-assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10^-58 ), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10^- 8), specificity (97.74% [96.98%-98.32%] vs 88.52% [87.05%-89.84%], p = 3.19 × 10^-58 ), and PPV (40.51% [29.79%-52.15%] vs 12.13% [8.61%-16.75%], p = 1.54 × 10^-8 ) in community-based screening. Sensitivity and NPV were not significantly differed. AI-assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases.

CONCLUSION

Our study provides a novel cytological method for detecting and screening early ESCC and HGIN.

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Simple Summary

This review discusses the timeline and development of the recommended screening tests, diagnosis system, and therapeutics implemented in clinics for precancer and cancer of the uterine cervix. The incorporation of the latest automation, machine learning modules, and state-of-the-art technologies into these aspects are also discussed.

Abstract

Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis. Although technical advancements have significantly improved the early diagnosis of cervical cancer, accurate diagnosis remains difficult owing to various factors. In recent years, artificial intelligence (AI)-based medical diagnostic applications have been on the rise and have excellent applicability in the screening and diagnosis of cervical cancer. Their benefits include reduced time consumption, reduced need for professional and technical personnel, and no bias owing to subjective factors. We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis. The application and challenges of using AI in the diagnosis and treatment of cervical cancer are also discussed.

Role of Artificial Intelligence Interpretation of Colposcopic Images in Cervical Cancer Screening

The accuracy of colposcopic diagnosis depends on the skill and proficiency of physicians. This study evaluated the feasibility of interpreting colposcopic images with the assistance of artificial intelligence (AI) for the diagnosis of high-grade cervical intraepithelial lesions. This study included female patients who underwent colposcopy-guided biopsy in 2020 at two institutions in the Republic of Korea. Two experienced colposcopists reviewed all images separately. The Cerviray AI® system (AIDOT, Seoul, Korea) was used to interpret the cervical images. AI demonstrated improved sensitivity with comparable specificity and positive predictive value when compared with the colposcopic impressions of each clinician. The areas under the curve were greater with combined impressions (both AI and that of the two colposcopists) of high-grade lesions, when compared with the individual impressions of each colposcopist. This study highlights the feasibility of the application of an AI system in cervical cancer screening. AI interpretation can be utilized as an assisting tool in combination with human colposcopic evaluation of exocervix.

How Can a High-Performance Screening Strategy Be Determined for Cervical Cancer Prevention? Evidence From a Hierarchical Clustering Analysis of a Multicentric Clinical Study

Background

This study aimed to explore the cluster patterns of cervical cancer screening strategies for detecting high-grade precancerous lesions in terms of benefits, costs, and efficiency.

Methods

A total of 2,065 referral women aged 25–64 years were recruited and underwent human papillomavirus (HPV) testing, liquid-based cytology with manual reading, and cytology with artificial intelligence (AI)-assisted reading. All women were assessed by colposcopy and histological examination. We formed 14 screening strategies based on primary cytology screening, primary HPV screening incorporating HPV-16/18 genotyping triage, cytology triage, or both, and co-testing. The primary outcomes were cervical intraepithelial neoplasia grade 2 or worse (CIN2+) and grade 3 or worse (CIN3+). The hierarchical clustering method was applied to multifaceted indicators, and then, the resulting clusters were described in terms of benefits, costs, efficiency, and their interaction. This study was registered (No. ChiCTR2000034131).

Results

The relative sensitivity of HPV-based strategies compared with cytology alone with the threshold of LSIL+ ranged from 0.68 to 1.19 for CIN2+ detection and from 0.72 to 1.11 for CIN3+ detection, whereas the relative specificity ranged from 0.55 to 1.43 for CIN2+ detection and from 0.51 to 1.51 for CIN3+ detection. Five significant clusters according to the trade-off among benefits, costs, and efficiency were identified. The cluster including four primary HPV screening strategies showed the optimal balance. HPV testing with HPV-16/18 genotyping and AI-based cytology triage presented the optimal trade-off for CIN3+ detection relative to cytology alone in terms of relative sensitivity (1.06), relative specificity (0.72), colposcopies for 1 CIN3+ (3.7 vs. 3.1), a load of follow-up for women with HPV-positive and normal cytology (7.0% vs. 22.3%), and the work of manual cytology reading (35.1% vs. 100%).

Conclusions

Our study provided clinical and methodological evidence on the choice of HPV-based screening strategies. The cluster including primary HPV screening with genotyping and cytology triage showed an optimal balance among benefit, cost, and efficiency.

Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis

Accurate early detection of breast and cervical cancer is vital for treatment success. Here, we conduct a meta-analysis to assess the diagnostic performance of deep learning (DL) algorithms for early breast and cervical cancer identification. Four subgroups are also investigated: cancer type (breast or cervical), validation type (internal or external), imaging modalities (mammography, ultrasound, cytology, or colposcopy), and DL algorithms versus clinicians. Thirty-five studies are deemed eligible for systematic review, 20 of which are meta-analyzed, with a pooled sensitivity of 88% (95% CI 85–90%), specificity of 84% (79–87%), and AUC of 0.92 (0.90–0.94). Acceptable diagnostic performance with analogous DL algorithms was highlighted across all subgroups. Therefore, DL algorithms could be useful for detecting breast and cervical cancer using medical imaging, having equivalent performance to human clinicians. However, this tentative assertion is based on studies with relatively poor designs and reporting, which likely caused bias and overestimated algorithm performance. Evidence-based, standardized guidelines around study methods and reporting are required to improve the quality of DL research.

The Performance of Artificial Intelligence in Cervical Colposcopy: A Retrospective Data Analysis

Objective

We aimed to evaluate the performance of artificial intelligence (AI) system in detecting high-grade precancerous lesions.

Methods

A retrospective and diagnostic study was conducted in Chongqing Cancer Hospital. Anonymized medical records with cytology, HPV testing, colposcopy findings with images, and the histopathological results were selected. The sensitivity, specificity, and areas under the curve (AUC) in detecting CIN2+ and CIN3+ were evaluated for the AI system, the AI-assisted colposcopy, and the human colposcopists, respectively.

Results

Anonymized medical records from 346 women were obtained. The images captured under colposcopy of 194 women were found positive by the AI system; 245 women were found positive either by human colposcopists or the AI system. In detecting CIN2+, the AI-assisted colposcopy significantly increased the sensitivity (96.6% vs. 88.8%, p=0.016). The specificity was significantly lower for AI-assisted colposcopy (38.1%), compared with human colposcopists (59.5%, p < 0.001) or the AI system (57.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.741, 0.765, and 0.674, respectively. In detecting CIN3+, the sensitivities of the AI system and AI-assisted colposcopy were not significantly higher than human colposcopists (97.5% vs. 92.6%, p=0.13). The specificity was significantly lower for AI-assisted colposcopy (37.4%) compared with human colposcopists (59.2%, p < 0.001) or compared with the AI system (56.6%, p < 0.001). The AUCs for the human colposcopists, AI system, and AI-assisted colposcopy were 0.759, 0.674, and 0.771, respectively.

Conclusions

The AI system provided equally matched sensitivity to human colposcopists in detecting CIN2+ and CIN3+. The AI-assisted colposcopy significantly improved the sensitivity in detecting CIN2+.

Development of a prognostic prediction support system for cervical intraepithelial neoplasia using artificial intelligence-based diagnosis

Objective

Human papillomavirus subtypes are predictive indicators of cervical intraepithelial neoplasia (CIN) progression. While colposcopy is also an essential part of cervical cancer prevention, its accuracy and reproducibility are limited because of subjective evaluation. This study aimed to develop an artificial intelligence (AI) algorithm that can accurately detect the optimal lesion associated with prognosis using colposcopic images of CIN2 patients by utilizing objective AI diagnosis.

Methods

We identified colposcopic findings associated with the prognosis of patients with CIN2. We developed a convolutional neural network that can automatically detect the rate of high-grade lesions in the uterovaginal area in 12 segments. We finally evaluated the detection accuracy of our AI algorithm compared with the scores by multiple gynecologic oncologists.

Results

High-grade lesion occupancy in the uterovaginal area detected by senior colposcopists was significantly correlated with the prognosis of patients with CIN2. The detection rate for high-grade lesions in 12 segments of the uterovaginal area by the AI system was 62.1% for recall, and the overall correct response rate was 89.7%. Moreover, the percentage of high-grade lesions detected by the AI system was significantly correlated with the rate detected by multiple gynecologic senior oncologists (r=0.61).

Conclusion

Our novel AI algorithm can accurately determine high-grade lesions associated with prognosis on colposcopic images, and these results provide an insight into the additional utility of colposcopy for the management of patients with CIN2.

High-grade lesion occupancy in the uterovaginal area was significantly correlated with CIN2 patients’ prognosis. The number of high-grade lesions in 12 segments detected by an artificial intelligence (AI)-based system was comparable to that detected by senior colposcopists. The overall correct response rate of the AI algorithm for detecting high-grade lesions was 89.7%.

Artificial Intelligence Assistive Technology in Hospital Professional Nursing Technology

Global aging is becoming more and more serious, and the nursing problems of the elderly will become very serious in the future. The article designs a control system with ATmega128 as the main controller based on the function of the multifunctional nursing robot. The article uses a convolutional neural network structure to estimate the position of 3D human joints. The article maps the joint coordinates of the colour map to the depth map based on the two camera parameters. At the same time, 15 joint heat maps are constructed with the joint depth map coordinates as the centre, and the joint heat map and the depth map are bound to the second-level neural network. The prediction of the position of the user's armpit is further completed by image processing technology. We compare this method with other attitude prediction methods to verify the advantages of this research method. The research background of this article is carried out in the context of global aging in the 21st century.

Feasibility and Accuracy of Artificial Intelligence-Assisted Sponge Cytology for Community-Based Esophageal Squamous Cell Carcinoma Screening in China

INTRODUCTION

Screening is the pivotal strategy to relieve the burden of esophageal squamous cell carcinoma (ESCC) in high-risk areas. The cost, invasiveness, and accessibility of esophagogastroduodenoscopy (EGD) necessitate the development of preliminary screening methods.

METHODS

Residents aged 40-85 years were recruited in a high-risk area of ESCC. Esophageal cells were collected using an approved novel capsule sponge, and cytology slides were scanned by a trained artificial intelligence (AI) system before cytologists provided confirmation. Atypical squamous cell or more severe diagnosis was defined as positive cytology. AI-based abnormal cell counts were also reported. EGD was performed subsequently with biopsy as needed. Diagnostic accuracy, adverse events, and acceptability of cytology testing were assessed. Esophageal high-grade lesions (ESCC and high-grade intraepithelial neoplasia) were the primary target lesions.

RESULTS

In total, 1,844 participants were enrolled, and 20 (1.1%) high-grade lesions were confirmed by endoscopic biopsy. The AI-assisted cytologist-confirmed cytology showed good diagnostic accuracy, with a sensitivity of 90.0% (95% confidence interval [CI], 76.9%-100.0%), specificity of 93.7% (95% CI, 92.6%-94.8%), and positive predictive value of 13.5% (95% CI, 7.70%-19.3%) for detecting high-grade lesions. The area under the receiver operation characteristics curve was 0.926 (95% CI, 0.850-1.000) and 0.949 (95% CI, 0.890-1.000) for AI-assisted cytologist-confirmed cytology and AI-based abnormal cell count, respectively. The numbers of EGD could be reduced by 92.5% (from 99.2 to 7.4 to detect 1 high-grade lesion) if only cytology-positive participants were referred to endoscopy. No serious adverse events were documented during the cell collection process, and 96.1% participants reported this process as acceptable.

DISCUSSION

The AI-assisted sponge cytology is feasible, safe, and acceptable for ESCC screening in community, with high accuracy for detecting esophageal squamous high-grade lesions.

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria. We train AIATBS with >81,000 retrospective samples. It integrates YOLOv3 for target detection, Xception and Patch-based models to boost target classification, and U-net for nucleus segmentation. We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic system which also includes a quality control solution. We validate the optimized system with >34,000 multicenter prospective samples and achieve better sensitivity compared to senior cytologists, yet retain high specificity while achieving a speed of <180s/slide. Our system is adaptive to sample preparation using different standards, staining protocols and scanners.

Application of artificial intelligence in gynecologic malignancies: A review

With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi-omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.

Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

OBJECTIVES

The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement.

MATERIALS AND METHODS

Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 % and 33 % of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of "kissing ovaries"; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 % threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5.

RESULTS

In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others.

CONCLUSIONS

The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.

Cervical cytology screening facilitated by an artificial intelligence microscope: A preliminary study

BACKGROUND

Cervical cytology screening is usually laborious with a heavy workload and poor diagnostic consistency. The authors have developed an artificial intelligence (AI) microscope that can provide onsite diagnostic assistance for cervical cytology screening in real time.

METHODS

A total of 2167 cervical cytology slides were selected from a cohort of 10,601 cases from Shenzhen Maternity and Child Healthcare Hospital, and the training data set consisted of 42,073 abnormal cervical epithelial cells. The recognition results of an AI technique were presented in a microscope eyepiece by an augmented reality technique. Potentially abnormal cells were highlighted with binary classification results in a 10× field of view (FOV) and with multiclassification results according to the Bethesda system in 20× and 40× FOVs. In addition, 486 slides were selected for the reader study to evaluate the performance of the AI microscope.

RESULTS

In the reader study, which compared manual reading with AI assistance, the sensitivities for the detection of low-grade squamous intraepithelial lesions and high-grade squamous intraepithelial lesions were significantly improved from 0.837 to 0.923 (P < .001) and from 0.830 to 0.917 (P < .01), respectively; the κ score for atypical squamous cells of undetermined significance (ASCUS) was improved from 0.581 to 0.637; the averaged pairwise κ of consistency for multiclassification was improved from 0.649 to 0.706; the averaged pairwise κ of consistency for binary classification was improved from 0.720 to 0.798; and the averaged pairwise κ of ASCUS was improved from 0.557 to 0.639.

CONCLUSIONS

The results of this study show that an AI microscope can provide real-time assistance for cervical cytology screening and improve the efficiency and accuracy of cervical cytology diagnosis.

Methodologies of Primary HPV Testing Currently Applied for Cervical Cancer Screening

The human papillomavirus is one of the most common sexually transmitted viruses, and an infection from this virus may become persistent, leading to diseases such as cervical cancer. In the past, cytology-based methods such as the Papanicolaou (Pap) test were imperative to identify the disease at a stage where it can be treated. However, since the 1980s where the etiological association of HPV and cervical cancer was identified, new tests began emerging directed towards identifying the virus. Furthermore, as the biology of HPV along with the relationships with its host are elucidated, these tests and treatments further advance. Recently in Europe, there is a movement towards the implementation of HPV testing methodologies in national screening programs to precede cytological testing. These screening strategies are recommended by the European guidelines and the World Health Organization. This review presents the current HPV testing methodologies, their application in organized population-based cervical cancer screening programs based on the most recent European guidelines, and their implementation status in countries in Europe.

Cervical cancer: Epidemiology, risk factors and screening

Cervical cancer is one of the leading causes of cancer death among females worldwide and its behavior epidemiologically likes a venereal disease of low infectiousness. Early age at first intercourse and multiple sexual partners have been shown to exert strong effects on risk. The wide differences in the incidence among different countries also influenced by the introduction of screening. Although the general picture remains one of decreasing incidence and mortality, there are signs of an increasing cervical cancer risk probably due to changes in sexual behavior. Smoking and human papillomavirus (HPV) 16/18 are currently important issues in a concept of multifactorial, stepwise carcinogenesis at the cervix uteri. Therefore, society-based preventive and control measures, screening activities and HPV vaccination are recommended. Cervical cancer screening methods have evolved from cell morphology observation to molecular testing. High-risk HPV genotyping and liquid-based cytology are common methods which have been widely recommended and used worldwide. In future, accurate, cheap, fast and easy-to-use methods would be more popular. Artificial intelligence also shows to be promising in cervical cancer screening by integrating image recognition with big data technology. Meanwhile, China has achieved numerous breakthroughs in cervical cancer prevention and control which could be a great demonstration for other developing and resource-limited areas. In conclusion, although cervical cancer threatens female health, it could be the first cancer that would be eliminated by human beings with comprehensive preventive and control strategy.