Diagnostic Precision Research Articles

Dynamic CT myocardial perfusion combined with coronary CT angiography for detecting hemodynamical significance of coronary artery stenosis: a comparative study.

This study aimed to evaluate the diagnostic performance of combined dynamic stress CT myocardial perfusion imaging (CTP) and coronary CT angiography (CTA) alongside CT-derived fractional flow reserve (CT-FFR) in detecting hemodynamically significant coronary artery disease (CAD). A total of 33 patients (86 vessels) who underwent coronary CTA, dynamic stress CTP, and coronary angiography were included. Vessels exhibiting 30-90% stenosis were subjected to FFR analysis based on coronary angiography (Angio-FFR). Hemodynamic significance, determined by Angio-FFR ≤ 0.80, and imaging findings were evaluated. The evaluation involved a comparison between the combined use of coronary CTA, CTP and CT-FFR, versus the sole use of coronary CTA. Out of 86 coronary vessels, 17 (19.8%) exhibited hemodynamically significant stenosis. The sensitivity, specificity, and accuracy of coronary CTA for detecting ischemia were 94.12%, 34.78%, and 46.51%, respectively. Adding CTP to CTA improved specificity to 88.41%, and accuracy to 87.21%, respectively. The area under the curve (AUC) for the discrimination of functional significant stenosis was 0.798 when using CTA alone, and for CTA plus CTP, it reached 0.910. Furthermore, the combination of CTA, CTP and CT-FFR, showed accuracy of 88.37%, sensitivity of 88.24% and specificity of 88.41% with the AUC of 0.946. The integration of dynamic CTP with CTA significantly enhances the diagnostic accuracy for identifying patients with hemodynamically significant CAD, compared to the use of CTA alone. This study underscores the value of combining CTP, CT-FFR, and CTA in improving diagnostic precision for CAD. The combination of CTP, CT-FFR and CTA offers a multifaceted assessment for patients with CAD by simultaneously providing anatomical detail, functional analysis, and physiological quantification, which facilitating rapid, accurate, and optimal clinical decision-making and significantly enhances patient management.

Management of acute chest pain in the Emergency Department: benefits of coronary computed tomography angiography.

Acute chest pain (ACP) is a frequent cause of Emergency Department (ED) admissions, with millions of cases reported globally each year. Timely and accurate diagnosis is crucial due to the wide range of underlying causes, such as acute coronary syndrome (ACS), pulmonary embolism (PE), aortic dissection, and others. Misdiagnosis can lead to missed life-saving interventions or, alternatively, unnecessary hospitalizations, escalating healthcare costs. While risk stratification tools like the HEART, GRACE, and TIMI scores are useful, additional imaging is often required to achieve diagnostic precision. Coronary computed tomography angiography (CCTA) has shown significant potential in enhancing diagnostic accuracy and improving patient outcomes. In this review, we explore the challenges physicians encounter when evaluating ACP in the ED, emphasizing the utility of CCTA as a key diagnostic tool. Additionally, we present a clinical case that illustrates how CT scan effectively aids in diagnosing patients with ambiguous symptoms, with CT imaging playing a pivotal role in identifying the underlying pathology.

Integration of radiomics ultrasound and TIRADS in diagnosis of thyroid nodules: a narrative review

Abstract Background The most popular technique for evaluating thyroid imaging is high-frequency ultrasonography; however, the TIRADS tool, intended to help with noninvasive risk assessment, has limitations in detecting thyroid cancerous nodules. The purpose of this article was to review the application of TIRADS in ultrasound radiomics and discuss its advantages and limitations. Main text A novel approach to medical picture processing called radiomics can help identify these nodules more precisely. Radiomics involves obtaining high-quality imaging for planning or diagnostic reasons, identifying a macroscopic tumor, extracting quantitative imaging features, and ranking the most informative findings according to prominence, independence, and reproducibility. Conclusions Radiomics combined with TIRADS has demonstrated strong potential in enhancing the accuracy of thyroid nodule malignancy predictions, though challenges such as the need for larger, diverse datasets, and thorough validation persist. Incorporating clinical data, using deep learning models, and standardized imaging protocols could improve diagnostic precision, and further research will, therefore, be essential for its implementation in routine clinical practice.

Instrumented Contact Lens to Detect Gaze Movements Independently of Eye Blinks.

To test on humans an eye tracker based on the use of a contact lens with active infrared sources and to evaluate whether this system can track the eye behind the eyelid. The system is made up of a scleral contact lens embedding two vertical cavity self-emitting lasers (VCSELs) remotely driven by eyewear comprised of electronics and cameras. Tests on humans were carried out on five subjects to assess performance and to determine whether the VCSEL spot could be detected behind the eyelid. The lens was worn and used without difficulty. The device allowed accurate tracking of the eye (0.83° ± 0.59°), and the VCSELs can be detected behind closed eyelids. These results confirm those previously obtained in terms of tracking and demonstrate that the device can be used safely to monitor eye movements even when the eyelids are closed. The VCSELs could be useful for a variety of applications to reduce data missed due to blinks, particularly with regard to interactive systems, fundamental studies on closed eye movements, or finding ways to improve clinical diagnostic precision for disorders of consciousness.

Feline Infectious Peritonitis Effusion Index: A Novel Diagnostic Method and Validation of Flow Cytometry-Based Delta Total Nucleated Cells Analysis on the Sysmex XN-1000V®

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led the medical and scientific community to explore the pathogenesis and clinical manifestations of coronaviruses. In felines, a widespread coronavirus known as feline coronavirus (FCoV) can lead to feline infectious peritonitis (FIP), a highly fatal disease characterised by severe systemic inflammation. Diagnosing FCoV remains challenging due to the limited accuracy of the available methods. The present study introduces the FIP Effusion Index, a novel diagnostic method that combines the albumin-to-globulin (ALB/GLOB) ratio with the delta total nucleated cell (∆TNC) count obtained via flow cytometry using the Sysmex XN-1000V® analyser in effusions. Samples from cats (n = 50) with suspected FIP were analysed for ∆TNC, with findings showing that a ∆TNC ≥ 2.1 is highly indicative of FIP and a ∆TNC ≥ 4.9 can be considered diagnostic. The FIP Effusion Index enhanced diagnostic precision in our group of samples, achieving 96.3% sensitivity and 95.7% specificity for values ≥ 5.06, and reaching perfect specificity (100%) with 96.3% sensitivity for values ≥ 7.54. This combined approach surpasses the accuracy of individual parameters, establishing the FIP Effusion Index as a superior diagnostic tool for FIP, with potential applications in both veterinary and human medicine for related coronavirus diseases.

PKCα Activation via the Thyroid Hormone Membrane Receptor Is Key to Thyroid Cancer Growth

Thyroid carcinoma (TC) is the most common endocrine neoplasia, with its incidence increasing in the last 40 years worldwide. The determination of genetic and/or protein markers for thyroid carcinoma could increase diagnostic precision. Accumulated evidence shows that Protein kinase C alpha (PKCα) contributes to tumorigenesis and therapy resistance in cancer. However, the role of PKCα in TC remains poorly studied. Our group and others have demonstrated that PKCs can mediate the proliferative effects of thyroid hormones (THs) through their membrane receptor, the integrin αvβ3, in several cancer types. We found that PKCα is overexpressed in TC cell lines, and it also appeared as the predominant expressed isoform in public databases of TC patients. PKCα-depleted cells significantly reduced THs-induced proliferation, mediated by the integrin αvβ3 receptor, through AKT and Erk activation. In databases of TC patients, higher PKCα expression was associated with lower overall survival. Further analyses showed a positive correlation between PKCα and genes from the MAPK and PI3K-Akt pathways. Finally, immunohistochemical analysis showed abnormal upregulation of PKCα in human thyroid tumors. Our findings establish a potential role for PKCα in the control of hormone-induced proliferation that can be explored as a therapeutic and/or diagnostic target for TC.

Single-point mutated lanmodulin as a high-performance MRI contrast agent for vascular and kidney imaging.

Magnetic resonance imaging contrast agents can enhance diagnostic precision but often face limitations such as short imaging windows, low tissue specificity, suboptimal contrast enhancement, or potential toxicity, which affect resolution and long-term monitoring. Here, we present a protein contrast agent based on lanmodulin, engineered with a single-point mutation at position 108 from N to D to yield maximum gadolinium binding sites. After loading with Gd3+ ions, the resulting protein complex, LanND-Gd, exhibits efficient renal clearance, high relaxivity, and prolonged renal retention compared to commercial agents. LanND-Gd enables high-performance visualization of whole-body structures and brain vasculature in male mice at a resolution finer than one hundred micrometers. In male ischemia mouse models, LanND-Gd also improves kidney dysfunction monitoring while minimizing risks of neural toxicity or immunogenic reactions. This protein-based contrast agent offers superior image quality, improved biocompatibility, and extended imaging timeframes, promising significant advancements in magnetic resonance-based diagnostics and patient outcomes.

Advances in wearable biotechnology for cardiac monitoring

Wearable biotechnology is enhancing cardiac health monitoring by enabling continuous data collection for early detection and better management of cardiovascular diseases (CVDs). Recent advancements, such as multi-sensor integration, combine electrocardiography (ECG), heart rate variability, blood pressure, and oxygen saturation monitoring to provide a comprehensive cardiovascular assessment and improve diagnostic precision. Innovations in energy-harvesting technologies, like thermoelectric and piezoelectric materials, extend battery life, reducing the need for frequent recharging and increasing patient adherence. Despite these advances, challenges remain, particularly concerning data security, which requires robust encryption methods and secure data transmission protocols. Regulatory frameworks like the GDPR and HIPAA need to evolve to accommodate technological progress, ensuring new devices are safe and quickly brought to market. Enhancing interoperability with electronic health records (EHRs) is crucial to maximizing the clinical utility of wearable devices. Future research should focus on developing biochemical sensors to detect specific biomarkers in bodily fluids, refining AI algorithms for personalized diagnostics, and establishing standards for data sharing and integration. Collaboration among healthcare providers, regulatory bodies, and technology developers is essential to overcoming current barriers, expanding clinical applications, and ultimately improving patient outcomes and public health.

Abstract 4141448: Evaluation of cardiac amyloidosis with cardiac MRI and association with endomyocardial biopsy findings

Introduction: Late gadolinium enhancement (LGE) observed on cardiac magnetic resonance imaging (CMR) has emerged as a potential indicator of myocardial involvement in cardiac amyloidosis (CA). However, discerning which LGE patterns in amyloidosis distinguish it from other cardiomyopathies remains unclear. This study aims to compare LGE characteristics in a predominantly African American cohort who underwent endomyocardial biopsies. Despite advancements in cardiac imaging techniques, the endomyocardial biopsy remains the gold standard for amyloidosis diagnosis. Insights garnered from this analysis could enhance diagnostic precision and facilitate earlier detection. Methods: In this retrospective study at MedStar Union Memorial Hospital in Baltimore, MD, patients who underwent both endomyocardial biopsy and CMR were included. The study compared individuals with biopsy-proven amyloidosis (both AL and ATTR) to those with negative biopsy results. LGE patterns were selected based on their frequent use in CMR interpretation. Statistical analysis employed Pearson chi-square and t-tests. Results: The cohort comprised 42 patients who underwent biopsy, with 13 patients (31%) with positive biopsy results. Among them, 24 were men (57%), and 81% identified as African American. Demographic, anthropomorphic, and objective clinical findings are detailed in Table 1. The average age of the cohort was 68 years, with those with biopsy-proven amyloidosis significantly older (75 vs. 61 years, p = 0.002). Although the biopsy-positive group exhibited a lower absolute NT-pro-BNP, the difference was not statistically significant. The most prevalent LGE patterns among the biopsy-positive group were diffuse (62%) and basal (31%). Notably, 69% of biopsy-positive patients exhibited an LGE pattern described as infiltrative. However, 38% of the biopsy-negative group also manifested infiltrative patterns on LGE, a difference not statistically significant compared to the biopsy-positive group. The positive predictive value of an infiltrative LGE pattern was low (45%). Conclusion: This study compares CMR LGE patterns in biopsy proven CA. Despite findings of an infiltrative LGE pattern on CMR, when using endomyocardial biopsy as the gold standard, the positive predictive value of LGE in differentiating CA patients was low. Further research and larger studies are warranted to explore additional imaging modalities or biomarkers that may complement or refine the diagnostic algorithm for CA.

Mammography In Breast Cancer Screening – Current Knowledge, Challenges, The Impact of Artificial Intelligence, And Effectiveness With A Focus On Poland

Mammography is a critical tool in breast cancer screening and secondary prevention, enabling early detection and significantly reducing breast cancer mortality. This literature review assesses the effectiveness of mammography in identifying early-stage tumors, discussing its advantages, limitations, and specific challenges. Additionally, advancements in imaging techniques, such as 3D tomosynthesis, are examined, especially for their enhanced sensitivity in women with dense breast tissue, which reduces structural overlap and improves diagnostic precision. The integration of artificial intelligence (AI) in mammographic image analysis opens new opportunities, supporting radiologists by automating the detection and classification of potential lesions, thereby increasing screening efficiency and reducing false-positive rates. However, effective implementation of AI requires seamless integration with clinical systems and ongoing research to refine algorithms for diverse clinical needs. Furthermore, legal frameworks are essential to delineate responsibility in AI-supported diagnostics. This review emphasizes the status of breast cancer screening in Poland, where participation rates remain low despite the availability of free screenings. This highlights the need for improved public awareness and accessibility, especially in underserved areas. Overall, the findings underscore the fundamental role of mammography in breast cancer detection, with AI and other technological advancements significantly enhancing diagnostic accuracy while identifying areas for further improvement.

Utilization of Computed Tomography at the Aydin Adnan Menderes University Faculty of Veterinary Medicine Research and Application Hospital: An Analysis of Preliminary Results on Patient Demographics and Case Distribution

This study examines the distribution of computed tomography (CT) usage at Aydin Adnan Menderes University Faculty of Veterinary Medicine Research and Application Hospital between 2023 and 2024. CT, a widely adopted diagnostic tool in veterinary medicine, allows for detailed imaging of internal organs and anatomical structures, enhancing the diagnosis of complex cases. The study analyzed data from 143 animals, including dogs, cats, and other species, to assess the frequency of CT scans based on species, age, sex, breed, and imaged anatomical regions. A total of 350 CT scans were reviewed, with dogs (n=87) undergoing the majority of scans (200), followed by cats (n=50) with 175 scans, and other species accounting for 26 scans. Cranium, thorax, and abdomen were the most frequently imaged regions across all species. The study found that older animals, particularly large-breed dogs and male cats, required more imaging, while younger animals (0-6 months) underwent fewer CT scans. The findings emphasize the need for tailored diagnostic approaches based on species, age, and sex to improve early detection and treatment outcomes in veterinary practice. Furthermore, the study highlights the potential for expanding CT use in non-traditional veterinary species, with the goal of improving diagnostic precision. The results provide important insights for enhancing veterinary services through the strategic application of CT imaging.

The role of MRI in muscle-invasive bladder cancer: an update from the last two years.

Muscle invasive bladder cancer (MIBC) is aggressive and requires radical cystectomy and neoadjuvant therapy, yet over 40% of patients face recurrence. The loss of the bladder also significantly reduces quality of life. Accurate staging, crucial for treatment decisions, is typically done through transurethral resection (TURBT), but inconsistencies in pathology affect diagnosis in 25% of cases. MRI is the most precise imaging method for evaluating local tumor invasiveness. This review discusses recent advances in MRI for staging MIBC and predicting responses to neoadjuvant therapy. Vesical imaging - reporting and data system (VI-RADS) accuracy may improve if combined with ADC maps and tumor contact length, while a bi-parametric MRI approach without contrast could reduce side effects without losing diagnostic precision, though evidence is mixed. VI-RADS shows promise in predicting neoadjuvant therapy responses, and the new nacVI-RADS score is in development. Non-Gaussian diffusion-weighted imaging techniques and machine learning could enhance accuracy but need more integration with mpMRI. VI-RADS may assist in evaluating responses in bladder-sparing regimens. Urodrill, an MRI-guided biopsy, aims to replace diagnostic TURBT but needs more accuracy data. MRI in MIBC is evolving, offering potential for accurate local staging and reduced side effects by avoiding TURBT. Predicting neoadjuvant treatment response could guide personalized treatment and bladder preservation. Larger trials are needed to validate these findings.

Evaluating the effectiveness of AI-powered UrologiQ's in accurately measuring kidney stone volume in urolithiasis patients.

Kidney stones and urolithiasis are kidney diseases that have a significant impact on health and well-being, and their incidence is increasing annually owing to factors such as age, sex, ethnicity, and geographical location. Accurate identification and volume measurement of kidney stones are critical for determining the appropriate surgical approach, as timely and precise treatment is essential to prevent complications and ensure successful outcomes. Larger stones often require more invasive procedures, and precise volume measurements are essential for effective surgical planning and patient outcomes. This study aimed to compare the ability of artificial intelligence (AI) to detect and measure kidney stone volume via CT-KUB images. CT KUB imaging data were analyzed to determine the effectiveness of AI in identifying the volume of kidney stones. The results were compared with measurements taken by radiologists. Compared with radiologists, the AI had greater accuracy, efficiency, and consistency in measuring kidney stone volume. The AI calculates the volume of kidney stones with an average difference of 80% compared with the volumes calculated by radiologists, highlighting a significant discrepancy that is critical for accurate surgical planning. The results suggest that artificial intelligence (AI) outperforms radiologists' manual calculations in measuring kidney stone volume. By integrating AI with kidney stone detection and treatment, there is potential for greater diagnostic precision and treatment effectiveness, which could ultimately improve patient outcomes.

RADT-23. MOLECULAR PROFILING OF GLIOMAS: INSIGHTS INTO RADIONECROSIS AND PSEUDOPROGRESSION THROUGH NEXT-GENERATION SEQUENCING

Abstract BACKGROUND Gliomas, the most common primary tumors of the CNS, presenting diagnostic and therapeutic challenges due to their heterogeneity. Next-generation sequencing (NGS) enhances diagnostic precision by identifying genetic alterations, guiding accurate diagnosis and personalized treatment strategies. Post-treatment complications such as radionecrosis and pseudoprogression present additional diagnostic difficulties, often mimicking tumor recurrence. This study aimed to evaluate the association between genetic alterations detected by NGS and treatment-related complications. METHODS This retrospective study analyzed data from electronic medical records at a Brazilian Radiation Therapy Department (2018-2023). The study included patients with gliomas who underwent surgical resection followed by adjuvant radiochemotherapy and subsequently developed complications such as radionecrosis and pseudoprogression. Genetic alterations detected by the HSL 500 (NGS) test were evaluated for their association with these complications using Fisher’s Exact Test. RESULTS Out of 192 patients treated for glioma, 46 (23.96%) experienced treatment-related complications, with 20 patients having available NGS test results. Among these, eight cases were identified as pseudoprogression, fifteen as radionecrosis, with three patients presenting both conditions. All 20 patients with NGS results were diagnosed with high-grade gliomas, eight of whom underwent reirradiation, resulting in two cases of radionecrosis post-reirradiation. Although no statistically significant association was found between genetic alterations and radionecrosis or pseudoprogression, specific mutations were prevalent in each group. In radionecrosis cases, the most common mutations were TERT promoter (c.1-124C>T) (n=9, p=0.613) and EGFR amplification (n=8, p=1.0). In pseudoprogression, frequent mutations included TERT promoter (c.1-124C>T) (n=6, p=0.642), EGFR (n=5, p=0.67), PTEN (n=4, p=1.0), NF1 (n=2, p=0.537), and P53 (n=2, p=0.197). CONCLUSION Molecular testing for glioma characterization is crucial for diagnosis and prognosis, aiding in understanding treatment-related complications. Strategies to avoid radionecrosis and pseudoprogression are vital to improve patient quality of life, especially in high-grade gliomas. Further studies are needed to confirm these trends and explore clinical relevance of mutations.

Emerging technologies and applications in multimodality imaging for ischemic heart disease: current state and future of artificial intelligence

Ischemic heart disease (IHD) is a major global health issue, frequently resulting in myocardial infarction and ischemic cardiomyopathy. Prompt and precise diagnosis is essential to avert complications such as heart failure and sudden cardiac death. Although invasive coronary angiography remains the gold standard for high-risk patients, noninvasive multimodality imaging is becoming more prevalent for those at low-to-intermediate risk. This review evaluated the current state of multimodality imaging in IHD, including echocardiography, nuclear cardiology, cardiac magnetic resonance imaging (MRI), computed tomography (CT) angiography, and invasive coronary angiography. Each modality has distinct strengths and limitations, and their complementary use provides a comprehensive assessment of cardiac health. Integrating artificial intelligence (AI) into imaging workflows holds promise for enhancing diagnostic accuracy and efficiency. AI algorithms can optimize image acquisition, processing, and interpretation of complex imaging data. Emerging technologies like 4D flow MRI, molecular imaging, and hybrid systems [e.g., positron emission tomography (PET)/MRI, PET/CT] integrate anatomical, functional, and molecular data, providing comprehensive insights into cardiac pathology and potentially revolutionizing the management of IHD. This review also explored the clinical applications and impact of multimodality imaging on patient outcomes, emphasizing its role in improving diagnostic precision and guiding therapeutic decisions. Future directions include AI-driven decision support systems and personalized medicine approaches. Addressing regulatory and ethical challenges, such as data privacy and algorithm transparency, is crucial for the broader adoption of these advanced technologies. This review highlighted the transformative potential of AI-enhanced multimodality imaging in improving the diagnosis and management of IHD.

A collagenase-decorated Cu-based nanotheranostics: remodeling extracellular matrix for optimizing cuproptosis and MRI in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC), characterized by a dense extracellular matrix (ECM), presents significant therapeutic challenges due to its poor prognosis and high resistance to chemotherapy. Current chemodrugs and diagnostic agents largely fail to cross the barrier posed by the ECM, which severely limits the PDAC theranostics. This study introduces a novel theranostic strategy using thioether-hybridized hollow mesoporous organosilica nanoparticles (dsMNs) for the co-delivery of copper (Cu) and disulfiram (DSF), aiming to induce cuproptosis in PDAC cells. Our approach leverages the ECM-degrading enzyme collagenase, integrated with dsMNs, to enhance drug penetration by reducing matrix stiffness. Furthermore, the innovative use of a pancreatic cancer cell membrane coating on the nanoparticles enhances tumor targeting and stability (dsMCu-D@M-Co). The multifunctional platform not only facilitates deep drug penetration and triggers cuproptosis effectively but also utilizes the inherent properties of Cu to serve as a T1-weighted magnetic resonance imaging (MRI) contrast agent. In vitro and in vivo assessments demonstrate significant tumor size reduction in PDAC-bearing mice, highlighting the dual functionality of our platform in improving therapeutic efficacy and diagnostic precision. This integrated strategy represents a significant advancement in the management of PDAC, offering a promising new direction for overcoming one of the most lethal cancers.Graphical

UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging

Background/Objectives: Breast cancer remains one of the biggest health challenges for women worldwide, and early detection can be truly lifesaving. Although ultrasound imaging is commonly used to detect tumors, the images are not always of sufficient quality, and, thus, traditional U-Net models often miss the finer details needed for accurate detection. This outcome can result in lower accuracy, making early and precise diagnosis more difficult. Methods: This study presents an enhanced U-Net model integrated with a Capsule Network (called UCapsNet) to overcome the limitations of conventional techniques. Our approach improves segmentation by leveraging higher filter counts and skip connections, while the capsule network enhances classification by preserving spatial hierarchies through dynamic routing. The proposed UCapsNet model operates in two stages: first, it segments tumor regions using an enhanced U-Net, followed by a classification of the segmented images with the capsule network. Results: We have tested our model against well-known pre-trained models, including VGG-19, DenseNet, MobileNet, ResNet-50, and Xception. By properly addressing the limitations found in previous studies and using a capsule network trained on the Breast Ultrasound Image (BUSI) dataset, our model resulted in top-achieving impressive precision, recall, and accuracy rates of 98.12%, 99.52%, and 99.22%, respectively. Conclusions: By combining the U-Net’s powerful segmentation capabilities with the capsule network’s high classification accuracy, UCapsNet boosts diagnostic precision and addresses key weaknesses in existing methods. The findings demonstrate that the proposed model is not only more effective in detecting tumors but also more reliable for practical applications in clinical settings.

AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

3D imaging techniques for the diagnosis and surgical planning of complex limb fractures: a systematic review

This systematic study will investigate how 3D imaging techniques have improved diagnosis and planning of surgeries for complex limb fractures. These fractures are caused of high-energy trauma which presents unique challenges, so advanced imaging technologies can help to mitigate them. We strictly adhere PRISMA guidelines to ensure thorough analysis and inclusion of relevant literature. The search was conducted on Web of Science and PubMed were all thoroughly searched between 2010 and 2023. Keyword combinations such as "3D imaging," "complex limb fractures," and "surgical planning" were employed using Boolean operators and as a result, 26 studies were included following screening based on strict inclusion criteria. 56% of studies used CT scans, demonstrating a 32% increase in diagnosis accuracy over 2D techniques for complicated fractures. In 28% of cases MRI improved soft tissue evaluation. 3D printing was utilized in 16% of investigations and it improved postoperative alignment in 94% of cases and shortened operating times by 18%. We can conclude that 3D imaging greatly enhances surgical planning, patient outcomes, and diagnostic precision in complex limb fractures and it provides useful resources for surgeons and patients by improving fracture visualization, reducing operating time, and promoting quicker recovery.

Functional connectivity and cognitive decline: a review of rs-fMRI, EEG, MEG, and graph theory approaches in aging and dementia

Age-related changes in the brain cause cognitive decline and dementia. In recent year’s researchers’ extensively studied the relationship between age related changes in functional connectivity (FC) in dementia. Those studies explore the alterations in FC patterns observed in aging and neurodegenerative disorders using techniques such as resting-state functional magnetic resonance imaging (rs-fMRI), electroencephalography (EEG) coherence analysis, and graph theory approaches. The current review summarizes the findings, which highlight the impact of FC changes on cognitive decline and neurodegenerative disease progression using these techniques and emphasize the importance of understanding neural alterations for early detection and intervention. The findings underscore the complexity of cognitive aging and the need for further research to differentiate normal aging from pathological conditions. rs-fMRI is essential for studying brain changes associated with aging and pathology by capturing coherent fluctuations in brain activity during rest, providing insights into FC without task-related confounds. Key networks such as the default mode network and front parietal control network are crucial in revealing age-related connectivity changes. Despite challenges like neurovascular uncoupling and data complexity, ongoing advancements promise improved clinical applications of rs-fMRI in understanding cognitive decline across the lifespan. EEG and magnetoencephalography (MEG) are cost-effective techniques with high temporal resolution, allowing detailed study of brain rhythms and FC. Recent studies highlight EEG/MEG’s potential in early Alzheimer’s disease detection by identifying changes in brain connectivity patterns. Integration of machine learning techniques enhances diagnostic accuracy, although further validation and research are necessary. Graph theory offers a quantitative framework to analyze cognitive networks, identifying distinct topological differences between healthy aging and pathological conditions. Future research should expand exploration into diverse neurodegenerative disorders beyond mild cognitive impairment, integrating neuroimaging techniques to refine diagnostic precision and deepen insights into brain function and connectivity.