Search Results (90)

In recent years, machine learning (ML) has been increasingly used in many fields, including medicine. Magnetic resonance imaging (MRI) is a non-invasive and effective diagnostic technique; however, manual image analysis is time-consuming and prone to human variability. In response, ML models have been developed to support MRI analysis, particularly in segmentation and classification tasks. This work presents an updated narrative review of ML applications in brain MRI, with a focus on tumor classification and segmentation. A literature search was conducted in PubMed and Scopus databases and Mendeley Catalog (MC)—a publicly accessible bibliographic catalog linked to Elsevier’s Scopus indexing system—covering the period from January 2020 to April 2025. The included studies focused on patients with primary or secondary brain neoplasms and applied machine learning techniques to MRI data for classification or segmentation purposes. Only original research articles written in English and reporting model validation were considered. Studies using animal models, non-imaging data, lacking proper validation, or without accessible full texts (e.g., abstract-only records or publications unavailable through institutional access) were excluded. In total, 108 studies met all inclusion criteria and were analyzed qualitatively. In general, models based on convolutional neural networks (CNNs) were found to dominate current research due to their ability to extract spatial features directly from imaging data. Reported classification accuracies ranged from 95% to 99%, while Dice coefficients for segmentation tasks varied between 0.83 and 0.94. Hybrid architectures (e.g., CNN-SVM, CNN-LSTM) achieved strong results in both classification and segmentation tasks, with accuracies above 95% and Dice scores around 0.90. Transformer-based models, such as the Swin Transformer, reached the highest performance, up to 99.9%. Despite high reported accuracy, challenges remain regarding overfitting, generalization to real-world clinical data, and lack of standardized evaluation protocols. Transfer learning and data augmentation were frequently applied to mitigate limited data availability, while radiomics-based models introduced new avenues for personalized diagnostics. ML has demonstrated substantial potential in enhancing brain MRI analysis and supporting clinical decision-making. Nevertheless, further progress requires rigorous clinical validation, methodological standardization, and comparative benchmarking to bridge the gap between research settings and practical deployment. Full article

Three-dimensional medical images, such as those obtained from MRI scans, offer a comprehensive view that aids in understanding complex shapes and abnormalities better than 2D images, such as X-ray, mammogram, ultrasound, and 2D CT slices. However, MRI machines are often inaccessible in certain regions due to their high cost, space and infrastructure requirements, a lack of skilled technicians, and safety concerns regarding metal implants. A viable alternative is generating 3D images from 2D scans, which can enhance medical analysis and diagnosis and also offer earlier detection of tumors and other abnormalities. This systematic review is focused on Generative Adversarial Networks (GANs) for 3D medical image analysis over the last three years, due to their dominant role in 3D medical imaging, offering unparalleled flexibility and adaptability for volumetric medical data, as compared to other generative models. GANs offer a promising solution by generating high-quality synthetic medical images, even with limited data, improving disease detection and classification. The existing surveys do not offer an up-to-date overview of the use of GANs in 3D medical imaging. This systematic review focuses on advancements in GAN technology for 3D medical imaging, analyzing studies, particularly from the recent years 2022–2025, and exploring applications, datasets, methods, algorithms, challenges, and outcomes. It affords particular focus to the modern GAN architectures, datasets, and codes that can be used for 3D medical imaging tasks, so readers looking to use GANs in their research could use this review to help them design their study. Based on PRISMA standards, five scientific databases were searched, including IEEE, Scopus, PubMed, Google Scholar, and Science Direct. A total of 1530 papers were retrieved on the basis of the inclusion criteria. The exclusion criteria were then applied, and after screening the title, abstract, and full-text volume, a total of 56 papers were extracted from these, which were then carefully studied. An overview of the various datasets that are used in 3D medical imaging is also presented. This paper concludes with a discussion of possible future work in this area. Full article

Background and objectives: Pain is a prevalent and often underestimated issue in children with cerebral palsy (CP). When left untreated, pain can result in secondary complications such as reduced mobility and mental health challenges, which negatively impact social activity, participation, and overall quality of life. This review explores the complex mechanisms underlying pain in CP, highlights contributing factors, and places particular emphasis on diagnostic challenges and multimodal pain management strategies. Methods: Three scientific databases and, additionally, guideline repositories (2015–2025) were searched, yielding 1335 records. Following a two-step deduplication process, 850 unique items remained. Eighty-five full texts were assessed, of which 49 studies were included. These comprised one randomised controlled trial, 16 non-randomised studies, 12 systematic reviews, 8 non-systematic reviews, and 12 guidelines or consensus statements. Methodological quality was appraised with AMSTAR-2 where applicable, and Oxford levels of evidence were assigned to all studies. Results: Study quality was variable: 25% were systematic reviews, with only one randomised controlled trial. This literature identifies overlapping nociceptive, neuropathic, and nociplastic mechanisms of pain development. Classification remains inconsistent, though the International Classification of Diseases provides a useful framework. Only five assessment tools have been validated for this population. Interventions were reported in 45% of studies, predominantly pharmacological (27%) and physiotherapeutic (23%). Evidence gaps remain substantial. Conclusions: This review highlights the complexity of pain in children and adolescents with cerebral palsy and the need for a biopsychosocial approach to assessment and management. Evidence supports individualised, multimodal strategies integrating physical therapies, contextual supports, and, where appropriate, medical or surgical interventions. Clinical implementation remains inconsistent due to limited high-quality evidence, inadequate assessment tools, and poor interdisciplinary integration. Full article

Background/Objectives: The maxillary labial frenum (MLF) is a connective tissue structure attaching the upper lip to the maxillary alveolar process. Its morphology varies significantly among individuals and is often most prominent during early childhood. While hypertrophic or low-attaching frena have been associated with diastemas, feeding issues, and speech impairments, there is no causal evidence supporting early surgical intervention. This review aims to examine current evidence regarding the timing and necessity of frenectomy procedures and to evaluate the implications of early versus delayed intervention. Methods: A narrative review was conducted using twenty peer-reviewed articles published in the past 10 years, with one additional article from 2012 included for its ongoing relevance. Databases searched included PubMed, the NIH database, the Reference Manual of Pediatric Dentistry, and journals from the American Academy of Pediatrics. Inclusion criteria were English-language, peer-reviewed studies that addressed the morphology, classification, diagnosis, management, and outcomes of MLFs across age groups. Excluded were studies focusing solely on mandibular, buccal, or lingual frena; non-English publications; case reports; and articles lacking full-text availability. Results: The literature suggests that premature frenectomy, prior to the eruption of permanent maxillary canines, typically between 9 and 12 years of age, is associated with frenum regrowth, surgical complications, and orthodontic relapse. Additionally, a lack of standardized diagnostic criteria contributes to inconsistent clinical decision-making. Conservative management, including monitoring, is strongly recommended as the frenum often migrates apically as the maxilla develops. Factors such as airway obstruction and developmental delays should be ruled out before considering surgery. Conclusions: There is insufficient evidence to support early surgical intervention for MLF-related concerns. A conservative, individualized approach, delaying frenectomy until after permanent canine eruption, may minimize complications, improve long-term outcomes, and allow the frenum to migrate apically as the patient develops. Standardized diagnostic tools are urgently needed to guide clinical decision-making. Full article

Background/Objectives: Oral lesions can present with a wide range of clinical appearances, often making diagnosis challenging, particularly for dental students. This study aimed to identify the most common oral lesions treated at a teaching dental clinic and to compare these findings with data from a systematic review of similar clinical settings. The goal was to inform and calibrate a clinical classification system for oral pathology used in teaching environments. Methods: A retrospective analysis was conducted using electronic medical records from a university dental clinic over the past 10 years. Oral and maxillofacial pathology cases were categorized based on clinical and histopathological diagnoses. A systematic review was also performed to provide external context, with searches conducted across four electronic databases. Two independent reviewers carried out the study selection, data extraction, and quality assessment. The review adhered to the PRISMA guidelines. Results: A total of 524 patients were identified with oral lesions. The most frequently encountered clinical diagnostic category was developmental defects, while the most common histopathological diagnosis from biopsied cases was epithelial atypia. The systematic review yielded 1215 records, of which 69 were retrieved for full-text assessment, and 28 studies met the inclusion criteria. Conclusions: The findings highlight the predominance of specific oral and maxillofacial pathoses in teaching clinic settings, underscoring the importance of targeted educational strategies to improve diagnostic confidence among students. There is also a need for more consistent diagnostic grouping in oral pathology to enable better comparison across studies and support clinical and pre-clinical teaching. By integrating these insights, we propose a referenced classification framework that may improve standardization in the clinical teaching of oral lesions and enhance diagnostic calibration and teaching effectiveness in dental education. Full article

The European e-Competence Framework (e-CF) and the European Skills, Competences, Qualifications and Occupations (ESCO) classification are two key initiatives developed by the European Commission to support skills transparency, mobility, and interoperability across labour and education systems. While e-CF defines essential competences for ICT professionals through a structured framework, it provides only a limited number of illustrative skills and knowledge examples for each competence. In contrast, ESCO offers a rich, multilingual taxonomy of skills and knowledge, each accompanied by a detailed description, alternative labels, and links to relevant occupations. This paper explores the possibility of enriching the e-CF framework by linking it to relevant ESCO ICT skills using text embedding (MPNet) and cosine similarity. This approach allows the extension to 15–25 semantically aligned skills and knowledge items per competence in e-CF, all with full description and officially translated into all EU languages, instead of the present amount of 4–10 brief examples. This significantly improves the clarity, usability, and interpretability of e-CF competences for the various stakeholders. Furthermore, since ESCO terminology serves as the foundation for labour market analysis across the EU, establishing this linkage provides a valuable bridge between the e-CF competence model and real-time labour market intelligence, a connection not available now. The results of this study offer practical insights into the application of semantic technologies to the enhancement and mutual alignment of European ICT skills frameworks. Full article

The volume and diversity of digital information have led to a growing reliance on Machine Learning (ML) techniques, such as Natural Language Processing (NLP), for interpreting and accessing appropriate data. While vector and graph embeddings represent data for similarity tasks, current state-of-the-art pipelines lack guaranteed explainability, failing to accurately determine similarity for given full texts. These considerations can also be applied to classifiers exploiting generative language models with logical prompts, which fail to correctly distinguish between logical implication, indifference, and inconsistency, despite being explicitly trained to recognise the first two classes. We present a novel pipeline designed for hybrid explainability to address this. Our methodology combines graphs and logic to produce First-Order Logic (FOL) representations, creating machine- and human-readable representations through Montague Grammar (MG). The preliminary results indicate the effectiveness of this approach in accurately capturing full text similarity. To the best of our knowledge, this is the first approach to differentiate between implication, inconsistency, and indifference for text classification tasks. To address the limitations of existing approaches, we use three self-contained datasets annotated for the former classification task to determine the suitability of these approaches in capturing sentence structure equivalence, logical connectives, and spatiotemporal reasoning. We also use these data to compare the proposed method with language models pre-trained for detecting sentence entailment. The results show that the proposed method outperforms state-of-the-art models, indicating that natural language understanding cannot be easily generalised by training over extensive document corpora. This work offers a step toward more transparent and reliable Information Retrieval (IR) from extensive textual data. Full article

Background/Objectives: Recurrent meningiomas remain difficult to manage due to the absence of effective systemic therapies and comparatively high treatment failure rates, particularly in high-grade tumors. Stereotactic radiosurgery (SRS) offers a minimally-invasive and precise option, particularly for tumors in surgically complex locations. However, the risks associated with re-irradiation, and recent changes in the WHO classification of CNS tumors highlight the need for more personalized and strategic treatment approaches. This systematic review evaluates the safety, efficacy, and clinical considerations for use of SRS for recurrent meningiomas. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted using the PubMed, Scopus, and Web of Science databases for studies reporting outcomes of SRS in recurrent, pathologically confirmed intracranial meningiomas. Studies were excluded if they were commentaries, reviews, case reports with fewer than three cases, or had inaccessible full text. The quality and risk of bias of the included studies were assessed using the modified Newcastle-Ottawa Scale. Data on patient and tumor characteristics, SRS treatment parameters, clinical outcomes, adverse effects, and statistical analysis results were extracted. Results: Sixteen studies were included. For WHO Grade I tumors, 3- to 5-year progression-free survival (PFS) ranged from 85% to 100%. Grade II meningiomas demonstrated more variable outcomes, with 3-year PFS ranging from 23% to 100%. Grade III tumors had consistently poorer outcomes, with reported 1-year and 2-year PFS rates as low as 0% and 46%, respectively. SRS performed after surgery alone was associated with superior outcomes, with local control rates of 79% to 100% and 5-year PFS ranging from 40.4% to 91%. In contrast, tumors previously treated with radiotherapy, with or without surgery, showed substantially poorer outcomes, with 3- to 5-year PFS ranging from 26% to 41% and local control rates as low as 31%. Among patients with prior radiotherapy, outcomes were particularly poor in Grade II and III recurrent tumors. Toxicity rates ranged from 3.7% to 37%, and were generally higher for patients with prior radiation. Predictors of worse PFS included prior radiation, older age, and Grade III histology. Conclusions: SRS may represent a reasonable salvage option for carefully selected patients with recurrent meningioma, particularly following surgery alone. Outcomes were notably worse in high-grade recurrent meningiomas following prior radiotherapy, emphasizing the prognostic significance of both histological grade and treatment history. Notably, the lack of molecular and genetic data in most existing studies represents a key limitation in the current literature. Future prospective studies incorporating molecular profiling may improve risk stratification and support more personalized treatment strategies. Full article

Background: Motor imagery (MI) is defined as the cognitive ability to simulate motor movements while suppressing muscular activity. The electroencephalographic (EEG) signals associated with lower limb MI have become essential in brain–computer interface (BCI) research aimed at assisting individuals with motor disabilities. Objective: This systematic review aims to evaluate methodologies for acquiring and processing EEG signals within brain–computer interface (BCI) applications to accurately identify lower limb MI. Methods: A systematic search in Scopus and IEEE Xplore identified 287 records on EEG-based lower-limb MI using artificial intelligence. Following PRISMA guidelines (non-registered), 35 studies met the inclusion criteria after screening and full-text review. Results: Among the selected studies, 85% applied machine or deep learning classifiers such as SVM, CNN, and LSTM, while 65% incorporated multimodal fusion strategies, and 50% implemented decomposition algorithms. These methods improved classification accuracy, signal interpretability, and real-time application potential. Nonetheless, methodological variability and a lack of standardization persist across studies, posing barriers to clinical implementation. Conclusions: AI-based EEG analysis effectively decodes lower-limb motor imagery. Future efforts should focus on harmonizing methods, standardizing datasets, and developing portable systems to improve neurorehabilitation outcomes. This review provides a foundation for advancing MI-based BCIs. Full article

Background: The U.S. Medicare Annual Wellness Visit (AWV) offers a structured opportunity for cognitive screening and personalized prevention planning among older adults. Yet, implementation of AWVs, particularly for individuals with cognitive impairment, remains inconsistent across primary care or other diverse care settings. Methods: We conducted a scoping review using the Consolidated Framework for Implementation Research (CFIR) to explore multilevel factors influencing the implementation of the Medicare AWV’s cognitive screening component, with a focus on how these processes support the detection and management of cognitive impairment among older adults. We searched four databases and screened peer-reviewed studies published between 2011 and March 2025. Searches were conducted in Ovid MEDLINE, PubMed, EBSCOhost, and CINAHL databases. The initial search was completed on 3 January 2024 and updated monthly through 30 March 2025. All retrieved citations were imported into EndNote 21, where duplicates were removed. We screened titles and abstracts for relevance using the predefined inclusion criteria. Full-text articles were then reviewed and scored as either relevant (1) or not relevant (0). Discrepancies were resolved through consensus discussions. To assess the methodological quality of the included studies, we used the Joanna Briggs Institute critical appraisal tools appropriate to each study design. These tools evaluate rigor, trustworthiness, relevance, and risk of bias. We extracted the following data from each included study: Author(s), year, title, and journal; Study type and design; Data collection methods and setting; Sample size and population characteristics; Outcome measures; Intervention details (AWV delivery context); and Reported facilitators, barriers, and outcomes related to AWV implementation. The first two authors independently coded and synthesized all relevant data using a table created in Microsoft Excel. The CFIR guided our data analysis, thematizing our findings into facilitators and barriers across its five domains, viz: (1) Intervention Characteristics, (2) Outer Setting, (3) Inner Setting, (4) Characteristics of Individuals, and (5) Implementation Process. Results: Among 19 included studies, most used quantitative designs and secondary data. Our CFIR-based synthesis revealed that AWV implementation is shaped by interdependent factors across five domains. Key facilitators included AWV adaptability, Electronic Health Record (EHR) integration, team-based workflows, policy alignment (e.g., Accountable Care Organization participation), and provider confidence. Barriers included vague Centers for Medicare and Medicaid Services (CMS) guidance, limited reimbursement, staffing shortages, workflow misalignment, and provider discomfort with cognitive screening. Implementation strategies were often poorly defined or inconsistently applied. Conclusions: Effective AWV delivery for older adults with cognitive impairment requires more than sound policy and intervention design; it demands organizational readiness, structured implementation, and engaged providers. Tailored training, leadership support, and integrated infrastructure are essential. These insights are relevant not only for U.S. Medicare but also for global efforts to integrate dementia-sensitive care into primary health systems. Our study has a few limitations that should be acknowledged. First, our scoping review synthesized findings predominantly from quantitative studies, with only two mixed-method studies and no studies using strictly qualitative methodologies. Second, few studies disaggregated findings by race, ethnicity, or geography, reducing our ability to assess equity-related outcomes. Moreover, few studies provided sufficient detail on the specific cognitive screening instruments used or on the scope and delivery of educational materials for patients and caregivers, limiting generalizability and implementation insights. Third, grey literature and non-peer-reviewed sources were not included. Fourth, although CFIR provided a comprehensive analytic structure, some studies did not explicitly fit in with our implementation frameworks, which required subjective mapping of findings to CFIR domains and may have introduced classification bias. Additionally, although our review did not quantitatively stratify findings by year, we observed that studies from more recent years were more likely to emphasize implementation facilitators (e.g., use of templates, workflow integration), whereas earlier studies often highlighted systemic barriers such as time constraints and provider unfamiliarity with AWV components. Finally, while our review focused specifically on AWV implementation in the United States, we recognize the value of comparative analysis with international contexts. This work was supported by a grant from the National Institute on Aging, National Institutes of Health (Grant No. 1R01AG083102-01; PIs: Tzeng, Kuo, & Raji). Full article

The operational roles of artificial intelligence in energy security remain inconsistently defined across the scientific literature. To address this gap, the present review examines 165 peer-reviewed abstracts published between 2021 and 2025 using a triangulated methodology that combines trigram frequency analysis, manual qualitative coding, and semantic clustering with sentence embeddings. Eight core roles were identified: forecasting and prediction, optimisation of energy systems, renewable energy integration, monitoring and anomaly detection, grid management and stability, energy market operations/trading, cybersecurity, and infrastructure and resource planning. According to the results, the most frequently identified roles, based on the average distribution across all three methods, are forecasting and prediction, optimisation of energy systems, and energy market operations/trading. Roles such as cybersecurity and infrastructure and resource planning appear less frequently and are primarily detected through manual interpretation and semantic clustering. Trigram analysis alone failed to capture these functions due to terminological ambiguity or diffuse expression. However, correlation coefficients indicate high concordance between manual and semantic methods (Spearman’s ρ = 0.91), confirming the robustness of the classification. A structured typology of AI roles supports the development of more coherent analytical frameworks in energy research. Future research incorporating full texts, policy taxonomies, and real-world use cases may help integrate AI more effectively into energy security planning and decision support environments. Full article

Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice. Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding. Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization. Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery. Full article

The ship waterjet propulsion system is a crucial power unit for high-performance vessels, and the operational state of its core component, the waterjet pump, is directly related to navigation safety and mission reliability. To enhance the intelligence and accuracy of pump fault diagnosis, this paper proposes a novel diagnostic framework that integrates a supervised autoencoder (SAE) with a large language model (LLM). This framework first employs an SAE to perform task-oriented feature learning on raw vibration signals collected from the pump’s guide vane casing. By jointly optimizing reconstruction and classification losses, the SAE extracts deep features that both represent the original signal information and exhibit high discriminability for different fault classes. Subsequently, the extracted feature vectors are converted into text sequences and fed into an LLM. Leveraging the powerful sequential information processing and generalization capabilities of LLM, end-to-end fault classification is achieved through parameter-efficient fine-tuning. This approach aims to avoid the traditional dependence on manually extracted time-domain and frequency-domain features, instead guiding the feature extraction process via supervised learning to make it more task-specific. To validate the effectiveness of the proposed method, we compare it with a baseline approach that uses manually extracted features. In two experimental scenarios, direct diagnosis with full data and transfer diagnosis under limited-data, cross-condition settings, the proposed method significantly outperforms the baseline in diagnostic accuracy. It demonstrates excellent performance in automated feature extraction, diagnostic precision, and small-sample data adaptability, offering new insights for the application of large-model techniques in critical equipment health management. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

Background/Objectives: Endometriosis can present as ovarian endometriosis in 15–25% of the cases. While chronic pelvic pain and dysmenorrhea dominate its clinical presentation, acute complications, such as spontaneous OMA rupture, are rare (<3%), often mimicking acute abdominal pain and necessitating emergency surgery. Diagnostic delays persist due to the condition’s rarity and overlapping symptoms with ovarian torsion or appendicitis. This study investigates the clinical features of ruptured OMAs to enhance preoperative suspicion and optimize management. Methods: From February 2011 to August 2023, 14 patients with spontaneous rupture of histologically confirmed endometriomas underwent emergency laparoscopy for acute abdominal pain in the University Hospital of Bern, Switzerland. The clinical data of these patients were analyzed to find common patterns of spontaneous endometrioma ruptures. We also conducted a literature search in PubMed, Scopus, ScienceDirect, Cochrane, and Embase databases from inception to December 2023 in order to identify other possible confounding factors. The search was based on the keywords “ruptured endometrioma”. All English full-text prospective and retrospective observational and interventional studies with at least five patients that described the clinical features and findings of women diagnosed with ruptured endometrioma and treated surgically were included. Results: The median age at operation was 37.4 (23–49) years old, and all cases presented with acute abdominal pain, with/without peritonitis. Only 3/14 patients presented with fever, while the most common laboratory finding was an elevated CRP level of 45.6 mg/L (3–100 mg/L), while leukocytosis was less pronounced, with a median of 12.2 G/L (6.04–21.4 G/L). Notably, 64.3% (9 out of 14) of the patients reported experiencing dysmenorrhea, while for the remaining 5 individuals, the presence or absence of dysmenorrhea could not be obtained. Interestingly, only one patient had undergone hormonal treatment, with a combined oral contraceptive (COC) of Ethinylestradiol (0.02 mg) and Desogestrel (0.15 mg), while the other patients either lacked awareness of their endometriosis or expressed reluctance towards hormonal downregulation therapy. The median endometrioma size was 7 cm (3.5–18 cm), and 78.57% of the cases (11 out of 14 patients) had only ovarian endometriosis, while only 3 patients had involvement of compartment A, B, or C according to the # ENZIAN classification. Conclusions: Though rare, spontaneous OMA rupture should be considered in acute abdomen cases, especially with cysts > 5 cm. Hormonal therapy may reduce rupture risk, but more research is needed to confirm this and refine diagnostic strategies. Full article