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.

Objective: Positron emission tomography with 2-deoxy-2-[fluorine-18] fluoro- D-glucose integrated with computed tomography (18F-FDG PET/CT) or magnetic resonance imaging (18F-FDG PET/MRI) has emerged as a promising tool for managing various types of cancer. This review study was conducted to investigate the role of 18F- FDG PET/CT and FDG PET/MRI in the management of gynecological malignancies. Search strategy: We searched for relevant articles in the three databases PubMed/MEDLINE, Scopus, and Web of Science. Selection criteria: All studies reporting data on the FDG PET/CT and FDG PET MRI in the management of gynecological cancer, performed anywhere in the world and published exclusively in the English language, were included in the present study. Data collection and analysis: We used the EndNote software (EndNote X8.1, Thomson Reuters) to list the studies and screen them on the basis of the inclusion criteria. Data, including first author, publication year, sample size, clinical application, imaging type, and main result, were extracted and tabulated in Excel. The sensitivity, specificity, and diagnostic accuracy of the modalities were extracted and summarized. Main results: After screening 988 records, 166 studies published between 2004 and 2022 were included, covering various methodologies. Studies were divided into the following five categories: the role of FDG PET/CT and FDG-PET/MRI in the management of: (a) endometrial cancer (n = 30); (b) ovarian cancer (n = 60); (c) cervical cancer (n = 50); (d) vulvar and vagina cancers (n = 12); and (e) gynecological cancers (n = 14). Conclusions: FDG PET/CT and FDG PET/MRI have demonstrated potential as non-invasive imaging tools for enhancing the management of gynecological malignancies. Nevertheless, certain associated challenges warrant attention.

Objective: This study is aimed at developing and evaluating a machine learning–based model for breast cancer classification using integrated clinical, demographic, reproductive, and lifestyle data. Methods: A retrospective machine learning framework was developed using data from a case–control study conducted in Tehran. The dataset included demographic, clinical, reproductive, lifestyle, and screening‐related variables. Data preprocessing was performed within machine learning pipelines to ensure data quality and prevent data leakage. Duplicate records and noninformative variables were removed. Missing values were imputed using median values for numerical variables and the most frequent value for categorical variables. Categorical features were encoded appropriately, and Min–Max normalization was applied where required. Feature selection was conducted using mutual information (MI) and analysis of variance (ANOVA) within a stratified cross‐validation framework. The data were split into training (80%) and test (20%) sets. Several supervised learning algorithms, including Gaussian Naive Bayes (GNB), K‐nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN), were trained and evaluated. Model performance was assessed using accuracy, precision, recall (sensitivity), F1‐score, and receiver operating characteristic–area under the curve (ROC‐AUC), with stratified 5‐fold cross‐validation and final evaluation on an independent test set. Results: Significant differences were observed between breast cancer patients and healthy controls across multiple demographic and clinical variables. Patients were generally older and more likely to be widowed, belong to higher socioeconomic classes, and be housewives, whereas higher education levels and employment were more frequent among healthy individuals ( p < 0.001). Reproductive factors, including age at first marriage and breastfeeding duration, also showed significant differences. Feature selection reduced 414 initial variables to 40 key predictors. The most influential features included genetic factors (BRCA1/2 mutations and family history), reproductive and hormonal characteristics (age at menarche, menopause, and infertility), lifestyle behaviors (dietary patterns and physical activity), anthropometric measures (BMI and weight at age 30), and screening‐related variables (mammography, ultrasound, and biopsy). All models demonstrated strong and stable performance with minimal differences between cross‐validation and test results, indicating good generalization. RF achieved the highest performance (accuracy: 0.9897, precision: 0.9946, recall: 0.9840, F1‐score: 0.9892), followed by SVM and LR, whereas ANN showed the lowest overall performance. Conclusion: Machine learning models can effectively classify breast cancer using multidimensional patient data. Ensemble methods, particularly RF, demonstrated superior accuracy and robustness, highlighting their ability to capture complex nonlinear relationships. The identified predictors are consistent with established clinical and epidemiological risk factors, supporting the validity of the proposed models. These findings suggest that machine learning approaches hold strong potential for personalized risk assessment and early detection of breast cancer; however, external validation across diverse populations is necessary to confirm generalizability.

Background: Accurate detection of ovarian cancer is crucial for effective treatment and patient survival. Objectives: This study aims to evaluate the diagnostic performance of convolutional neural network (CNN) algorithms for the identification of ovarian cancer. Search strategy: In this systematic review with meta-analysis, we conducted a comprehensive literature search across four electronic databases: Medline (PubMed), Scopus, Embase, and Web of Science (WOS) in June 2024 and was subsequently updated on 1 February 2026. The search strategy was developed in consultation with domain experts and information specialists to maximize both sensitivity (SE) and specificity (SP). A combination of Medical Subject Headings (MeSH) and free-text terms related to “ovarian cancer,” “convolutional neural networks,” “deep learning,” and “artificial intelligence” was used, with Boolean operators (“AND,” “OR”) applied to combine search terms effectively. Selection criteria: Our review included all observational studies evaluating CNN algorithms for ovarian cancer detection, regardless of geographical location. Study selection was managed using EndNote and involved a two-step screening process, with titles/abstracts and full texts independently assessed by reviewers. Studies reporting the diagnostic performance of CNN algorithms for histopathologically confirmed ovarian cancer were eligible for inclusion. For the meta-analysis, we included studies that provided extractable data on true positives, false positives, true negatives, and false negatives, or threshold-specific SE and SP that could be converted into a 2 × 2 format. Data collection and analysis: Data were analyzed using R (version 4.2.3). Pooled SE, SP, and Area Under the Curve (AUC) were calculated using a multilevel hierarchical model with a study-level random effect. Four subgroups—imaging modalities, CNN architectures, learning algorithms, and database types—were investigated. Meta-regression was performed, and potential publication bias was assessed using Deeks’ funnel plot of log(DOR) versus 1/Effective Sample Size. Results: Following a review of 1,043 publications on CNN algorithms for ovarian cancer detection, 47 studies were included in the systematic review and 20 in the meta-analysis. Pooled analysis showed that CNN algorithms achieved a SE of 0.94 (95% CI 0.92–0.96), SP of 0.95 (95% CI 0.90–0.97), and an AUC of 0.974 (95% CI 0.961–0.981). Among imaging modalities, magnetic resonance imaging (MRI) demonstrated the highest diagnostic performance (SE 0.97, SP 0.955, AUC 0.986), followed by computed tomography (CT) (SE 0.914, SP 0.975, AUC 0.983), histopathology (SE 0.979, SP 0.934, AUC 0.981), and ultrasound (SE 0.891, SP 0.951, AUC 0.922). Among CNN architectures, other architectures achieved the highest pooled AUC (0.979), followed by ResNet (SE 0.92, SP 0.947, AUC 0.969) and DenseNet (SE 0.927, SP 0.932, AUC 0.956). Transfer learning (SE 0.942, SP 0.948, AUC 0.978) outperformed fully trained models (SE 0.959, SP 0.929, AUC 0.962). Open-source datasets showed higher performance (SE 0.98, SP 0.971, AUC 0.985) than non-open datasets (SE 0.931, SP 0.938, AUC 0.968). Meta-regression indicated that the “other” algorithm family was significantly associated with higher logDOR, while imaging modality, dataset openness, transfer learning, and DenseNet were not significant predictors. Substantial heterogeneity remained across studies, but leave-one-out analysis confirmed the robustness of the pooled estimates, and Deeks’ test suggested potential publication bias. Conclusion: CNN-based algorithms demonstrate high diagnostic accuracy for ovarian cancer detection, with particularly strong performance across imaging modalities such as MRI, CT, and histopathology. These findings highlight the potential of deep learning models to support AI-assisted diagnostic workflows and improve early detection. However, substantial heterogeneity across studies and potential publication bias indicate the need for standardized imaging protocols, larger multi-center datasets, and external validation. Future research should focus on harmonizing data sources and integrating CNN-based tools into clinical decision-making to enhance diagnostic reliability and patient outcomes.