Search Results (4,221)

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Introduction: This study evaluated the performance of automated dual-energy computed tomography (DECT)-based quantification of perfusion defects (PDs) in acute pulmonary embolism and examined its correlation with clinical parameters. Methods: We retrospectively analyzed data from 171 patients treated for moderate-to-severe acute pulmonary embolism, who underwent DECT imaging at two separate time points. PDs were quantified using a fully automated AI-based segmentation method that relied exclusively on iodine perfusion maps. This was compared with a semi-automatic clinician-guided segmentation, where radiologists manually adjusted thresholds to eliminate artifacts. Clinical variables including the Miller obstruction score, right-to-left ventricular diameter ratio, oxygen saturation, and patient-reported symptoms were also collected. Results: The semiautomatic method demonstrated stronger correlations with embolic burden (Miller score; r = 0.4, p < 0.001 at follow-up) and a negative correlation with oxygen saturation (r = −0.2, p = 0.04). In contrast, the fully automated AI-based quantification consistently produced lower PD values and demonstrated weaker associations with clinical parameters. Conclusions: Semiautomatic quantification of PDs currently provides superior accuracy and clinical relevance for evaluating lung PDs in acute pulmonary embolism. Future multimodal AI models that incorporate both anatomical and clinical data may further enhance diagnostic precision. Full article

Background/Objectives: While intravenous thrombolysis (IVT) is the standard treatment for acute ischemic stroke (AIS) within 4.5 h of symptom onset, many patients present beyond this time window. Recent trials suggest that IVT may be both effective and safe in selected patients treated after the standard time window. Methods: We searched MEDLINE, Scopus, and ClinicalTrials.gov for randomized-controlled clinical trials (RCTs) and individual patient-data meta-analyses (IPDMs) of RCTs comparing IVT plus best medical treatment (BMT) to BMT alone in AIS patients who were last-known-well more than 4.5 h earlier. The primary efficacy outcome was a 90-day excellent functional outcome [modified Rankin Scale (mRS)-scores of 0–1]. Secondary efficacy outcomes included good functional outcome (mRS-scores 0–2) and reduced disability (≥1-point reduction across all mRS-strata). The primary safety outcome was symptomatic intracranial hemorrhage (sICH); secondary safety outcomes were any ICH and 3-month all-cause mortality. Subgroup analyses were performed stratified by different thrombolytics, time-windows, imaging modalities, and affected circulation. Results: Nine studies were included, comprising 1660 patients in the IVT-group and 1626 patients in the control-group. IVT significantly improved excellent functional outcome (RR = 1.24; 95%CI:1.14–1.34; I² = 0%) and good functional outcome (RR = 1.18; 95%CI:1.05–1.33; I² = 70%). IVT was associated with increased odds of reduced disability (common OR = 1.3; 95%CI:1.15–1.46; I² = 0%) and increased risk of sICH (RR = 2.75; 95%CI:1.49–5.05; I² = 0%). The rates of any ICH and all-cause mortality were similar between the two groups. No significant subgroup differences were documented. Conclusions: IVT in the extended time window improved functional outcomes without increasing mortality, despite a higher rate of sICH. Full article

Background/Objectives: Pituitary tumors account for over 90% of all sellar region masses. However, a spectrum of rare neoplastic, inflammatory, infectious, and vascular lesions—benign and malignant—can arise in the intra- and parasellar compartments and clinically and radiologically mimic PitNETs. We report a cohort of 47 such rare and cystic midline intracranial lesions, emphasizing their distinctive morphological, clinical, and imaging features and the personalized treatment strategies applied. Methods: In this retrospective single-center study, we reviewed all patients treated for suspected PitNETs via transsphenoidal approach between 2015 and 2024. Of 529 surgical cases, we excluded confirmed PitNETs, meningiomas, and classical intradural craniopharyngiomas. Collected data encompassed patient demographics, tumor characteristics, presenting symptoms, extent of resection or medical therapy, endocrine outcomes, and follow-up information. Results: Among all 529 patients who underwent surgical treatment for sellar lesions from 2015 to 2024, 47 cases (8.9%) were identified as rare or cystic masses. Forty-six underwent transsphenoidal resection; one patient with hypophysitis received corticosteroid therapy alone. Presenting symptoms included headache (n = 16), dizziness (n = 5), oculomotor disturbances (n = 2), and visual impairment (n = 17). Endocrine dysfunction was found in 30 patients, 27 of whom required hydrocortisone replacement. Histopathological diagnoses were led by colloid cysts (n = 14) and Rathke’s cleft cysts (n = 11). The remaining 22 cases comprised plasmacytoma, germinoma, lymphoma, pituicytoma, inverted papilloma, metastatic carcinoma, chordoma, nasopharyngeal carcinoma, chloroma, and other rare entities. Preoperative imaging diagnosis proved incorrect in 38% (18/47) of cases, with several lesions initially misidentified as PitNETs. Conclusions: Nearly 9% of presumed PitNETs were rare, often benign or inflammatory lesions requiring distinct management. Most could be safely resected and demonstrated excellent long-term outcomes. Yet, despite advanced imaging techniques, accurate preoperative differentiation remains challenging, with over one-third misdiagnosed. Clinical red flags—such as early hormone deficits, rapid progression or atypical imaging findings—should prompt early interdisciplinary evaluation and, when indicated, image-guided biopsy to avoid unnecessary surgery and ensure tailored therapy. Full article

Background/Objectives: The integration of artificial intelligence (AI) in medical imaging is rapidly advancing, yet its application in gynecologic use remains limited. This proof-of-concept study presents the development and validation of a convolutional neural network (CNN) designed to automatically detect and classify endometrial polyps. Methods: A multicenter dataset (n = 3) comprising 65 hysteroscopies was used, yielding 33,239 frames and 37,512 annotated objects. Still frames were extracted from full-length videos and annotated for the presence of histologically confirmed polyps. A YOLOv1-based object detection model was used with a 70–20–10 split for training, validation, and testing. Primary performance metrics included recall, precision, and mean average precision at an intersection over union (IoU) ≥ 0.50 (mAP50). Frame-level classification metrics were also computed to evaluate clinical applicability. Results: The model achieved a recall of 0.96 and precision of 0.95 for polyp detection, with a mAP50 of 0.98. At the frame level, mean recall was 0.75, precision 0.98, and F1 score 0.82, confirming high detection and classification performance. Conclusions: This study presents a CNN trained on multicenter, real-world data that detects and classifies polyps simultaneously with high diagnostic and localization performance, supported by explainable AI features that enhance its clinical integration and technological readiness. Although currently limited to binary classification, this study demonstrates the feasibility and potential of AI to reduce diagnostic subjectivity and inter-observer variability in hysteroscopy. Future work will focus on expanding the model’s capabilities to classify a broader range of endometrial pathologies, enhance generalizability, and validate performance in real-time clinical settings. Full article

Background: Pancreatic cystic lesions (PCLs), including intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs), pose a diagnostic challenge due to their variable malignant potential. Current guidelines, such as Fukuoka and American Gastroenterological Association (AGA), have moderate predictive accuracy and may lead to overtreatment or missed malignancies. Artificial intelligence (AI), incorporating machine learning (ML) and deep learning (DL), offers the potential to improve risk stratification, diagnosis, and management of PCLs by integrating clinical, radiological, and molecular data. This is the first systematic review to evaluate the application, performance, and clinical utility of AI models in the diagnosis, classification, prognosis, and management of pancreatic cysts. Methods: A systematic review was conducted in accordance with PRISMA guidelines and registered on PROSPERO (CRD420251008593). Databases searched included PubMed, EMBASE, Scopus, and Cochrane Library up to March 2025. The inclusion criteria encompassed original studies employing AI, ML, or DL in human subjects with pancreatic cysts, evaluating diagnostic, classification, or prognostic outcomes. Data were extracted on the study design, imaging modality, model type, sample size, performance metrics (accuracy, sensitivity, specificity, and area under the curve (AUC)), and validation methods. Study quality and bias were assessed using the PROBAST and adherence to TRIPOD reporting guidelines. Results: From 847 records, 31 studies met the inclusion criteria. Most were retrospective observational (n = 27, 87%) and focused on preoperative diagnostic applications (n = 30, 97%), with only one addressing prognosis. Imaging modalities included Computed Tomography (CT) (48%), endoscopic ultrasound (EUS) (26%), and Magnetic Resonance Imaging (MRI) (9.7%). Neural networks, particularly convolutional neural networks (CNNs), were the most common AI models (n = 16), followed by logistic regression (n = 4) and support vector machines (n = 3). The median reported AUC across studies was 0.912, with 55% of models achieving AUC ≥ 0.80. The models outperformed clinicians or existing guidelines in 11 studies. IPMN stratification and subtype classification were common focuses, with CNN-based EUS models achieving accuracies of up to 99.6%. Only 10 studies (32%) performed external validation. The risk of bias was high in 93.5% of studies, and TRIPOD adherence averaged 48%. Conclusions: AI demonstrates strong potential in improving the diagnosis and risk stratification of pancreatic cysts, with several models outperforming current clinical guidelines and human readers. However, widespread clinical adoption is hindered by high risk of bias, lack of external validation, and limited interpretability of complex models. Future work should prioritise multicentre prospective studies, standardised model reporting, and development of interpretable, externally validated tools to support clinical integration. Full article

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than 200 pregnant women (in the second and third trimesters) reveals significant increases in the umbilical arterial peak systolic velocity ($PSV$) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress ($TAWSS$), oscillatory shear index ($OSI$), relative residence time ($RRT$), Reynolds number ($Re$), and Dean number ($De$), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring. Full article

Background/Objectives: Papillary thyroid microcarcinoma (MPTC), a subset of papillary thyroid carcinoma (PTC), is increasingly detected with advanced imaging. While most MPTCs are indolent, some exhibit aggressive behavior, complicating clinical management. The BRAF V600E mutation, common in PTC, is linked to aggressive features, and its allele frequency (AF) may serve as a biomarker for tumor aggressiveness. This study explored the association between BRAF V600E AF and aggressive histopathological features in MPTC. Methods: Data from 1 January 2016 to 23 December 2023 were retrieved from two McGill University teaching hospitals. Inclusion criteria comprised patients aged ≥ 18 years with thyroid nodules ≤ 1 cm, documented BRAF V600E mutation and AF results, and available surgical pathology reports. Tumor aggressiveness was defined as the presence of lymph node metastasis, aggressive histological subtype (tall cell, hobnail, columnar, solid/trabecular or diffuse sclerosing), extra thyroidal extension, or extensive lymphovascular extension. Associations were explored using t-tests. Results: Among 1564 records, 34 met the inclusion criteria and were included in analyses. The mean BRAF V600E AF was significantly higher in aggressive tumors (23.58) compared to non-aggressive tumors (13.73) (95% CI: −18.53 to −1.16, p = 0.03). Although not statistically significant, trends were observed for higher BRAF V600E AF in tumors with lymph node metastasis (mean AF: 25.4) compared to those without (mean AF: 16.67, p = 0.08). No significant difference was noted in BRAF V600E AF by histological subtype (mean AF for aggressive: 19.57; non-aggressive: 19.15, p = 0.94). Conclusions: Elevated BRAF V600E AF is associated with aggressive behavior in MPTC, highlighting its potential as a biomarker to inform treatment strategies. Larger studies are warranted to validate these findings and enhance clinical management of MPTC patients. Full article

Background: Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of severe aortic valve stenosis (AS). Balloon post-dilatation (PD) remains an important procedural step to optimize valve function by resolving incomplete valve expansion, which may lead to paravalvular regurgitation and other potentially adverse effects. There are only limited data on the predictors, incidence, and clinical impact of PD during TAVI. Methods: This retrospective, single-center study analyzed 585 patients who underwent TAVI (2016–2022). Pre-procedural evaluations included transthoracic echocardiography and CT angiography to assess key parameters, including the aortic valve calcium score (AVCS); aortic valve calcium density (AVCd); aortic valve maximal systolic transvalvular flow velocity (AV Vmax); and aortic valve mean systolic pressure gradient (AV MPG). We identified imaging predictors of PD and evaluated associated clinical outcomes by analyzing procedural endpoints (according to VARC-3 criteria) and long-term survival. Results: PD was performed on 67 out of 585 patients, with elevated AV Vmax (OR: 1.424, 95% CI: 1.039–1.950; p = 0.028) and AVCd (OR: 1.618, 95% CI: 1.227–2.132; p = 0.001) emerging as a significant independent predictor for PD in TAVI. Kaplan–Meier survival analysis revealed no significant differences in short- and mid-term survival between patients who underwent PD and those who did not. Interestingly, patients requiring PD exhibited a lower incidence of adverse events regarding major vascular complications, permanent pacemaker implantations and stroke. Conclusions: The study highlights AV Vmax and AVCd as key predictors of PD. Importantly, PD was not associated with increased procedural adverse events and did not predict adverse events in this contemporary cohort. Full article

Background/Objectives: Benign paroxysmal positional vertigo (BPPV) is the most prevalent vestibular disorder encountered in clinical settings and is highly responsive to evidence-based diagnostic and therapeutic interventions. However, variations in practice patterns among physician specialties can compromise timely diagnosis and effective treatment. Understanding these variations is essential for improving clinical outcomes and standardizing care. This study aimed to assess the diagnostic and treatment practices for BPPV among Ear, Nose, and Throat (ENT) specialists, neurologists, general practitioners, and family physicians in Saudi Arabia and to examine how these practices are influenced by clinical experience and patient case exposure. Methods: A cross-sectional, questionnaire-based study was conducted between April 2023 and March 2024 at King Khalid University, Abha, Saudi Arabia. A total of 413 physicians were recruited using purposive sampling. Data were analyzed using IBM SPSS version 24.0. Parametric tests, including one-way ANOVA and chi-square tests, were used to assess differences across groups. A p-value of <0.05 was considered statistically significant. Results: Overall, all physician groups exhibited limited adherence to guideline-recommended positional diagnostic and therapeutic maneuvers. However, ENT specialists and neurologists demonstrated relatively higher compliance, particularly in performing the Dix–Hallpike test, with 46.97% and 26.79% reporting “always” using the maneuver, respectively (p < 0.001, Cramér’s V = 0.22). Neurologists were the most consistent in conducting oculomotor examinations, with 73.68% reporting routine performance (p < 0.001, Cramér’s V = 0.35). Epley maneuver usage was highest among neurologists (86.36%) and ENT specialists (77.14%) compared to family physicians (50.60%) and GPs (67.50%) (p = 0.044). Physicians with 11–15 years of experience and >50 BPPV case exposures consistently showed a greater use of diagnostic maneuvers, repositioning techniques, and guideline-concordant medication use (betahistine 76.67%; p < 0.001). Continuing medical education (CME) participation and the avoidance of unnecessary imaging were also highest in this group (46.67% and 3.33%, respectively; p < 0.001). Conclusions: Significant inter-specialty differences exist in the management of BPPV in Saudi Arabia. Greater clinical experience and higher case exposure are associated with improved adherence to evidence-based practices. Targeted educational interventions are needed, particularly in primary care, to enhance guideline implementation. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Objectives: Automatic tip localization is critical in ultrasound (US)-guided interventions. Although deep learning (DL) has been widely used for precise tip detection, existing methods are limited by the availability of real puncture data and expert annotations. Methods: To address these challenges, we propose a novel method that uses synthetic US puncture data to pre-train DL-based tip detectors, improving their generalization. Synthetic data are generated by fusing clinical US images of healthy controls with tips created using generative DL models. To ensure clinical diversity, we constructed a dataset from scans of 20 volunteers, covering 20 organs or anatomical regions, obtained with six different US machines and performed by three physicians with varying expertise levels. Tip diversity is introduced by generating a wide range of synthetic tips using a denoising probabilistic diffusion model (DDPM). This method synthesizes a large volume of diverse US puncture data, which are used to pre-train tip detectors, followed by subsequently training with real puncture data. Results: Our method outperforms MSCOCO pre-training on a clinical puncture dataset, achieving a 1.27–7.19% improvement in AP_0.1:0.5 with varying numbers of real samples. State-of-the-art detectors also show performance gains of 1.14–1.76% when applying the proposed method. Conclusions: The experimental results demonstrate that our method enhances the generalization of tip detectors without relying on expert annotations or large amounts of real data, offering significant potential for more accurate visual guidance during US-guided interventions and broader clinical applications. Full article

Background/Objectives: There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. Methods: We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick’s gray level co-occurrence matrix (GLCM) textural features). Results: Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8–128, while preserving trends. Conclusions: When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images. Full article

Background: Anti-synthetase Syndrome (ASyS) is an idiopathic inflammatory myopathy characterized by muscle weakness and inflammatory infiltrates in muscles. Sjogren’s disease (SD) is an autoimmune condition primarily affecting exocrine glands. Both these conditions may present lung involvement. We describe a female patient with anti-synthetase/SD overlap syndrome and review the literature to identify published cases describing this overlap, aiming to better define its clinical, radiological, and serological features. Methods: The case description was based on a retrospective collection of clinical, laboratory, and imaging data related to the patient’s diagnostic process and clinical course. Data were anonymized and handled in accordance with the competent territorial Ethics Committee. A literature review was performed using the MEDLINE and Scopus databases by combining the keywords “Anti-Synthetase syndrome”, “Sjögren disease”, “Sjögren syndrome”, “Myositis”, and “Interstitial lung disease” (ILD). Published cases were selected if they met the 2016 EULAR/ACR criteria for SD and at least one of the currently proposed classification criteria for ASyS. Results: The described case concerns a 68-year-old woman with rapidly progressive ILD. The diagnosis of anti-synthetase/SD overlap syndrome was based on clinical, serological (anti-Ro52 and anti-PL7 antibodies), histological, and radiological findings. Despite immunosuppressive and antifibrotic treatment, the clinical course worsened, leading to a poor outcome. In addition, six relevant cases were identified in the literature. Clinical presentations, autoantibody profiles, radiological findings, and outcomes were highly heterogeneous. Among the reported cases, no standardized treatment protocols were adopted, reflecting the lack of consensus in managing this rare condition. Conclusions: In anti-synthetase/SD overlap syndrome, ILD may follow a rapidly progressive course. Early recognition can be challenging, especially in the absence of muscular involvement. This case-based review highlights the need for more standardized approaches to the diagnosis and management of this rare and complex overlap syndrome. Full article

Background/Objectives: Brainstem ischemic strokes comprise 10% of ischemic strokes and are challenging to diagnose due to small lesion size and complex presentations. Diffusion-weighted imaging (DWI) is crucial for detecting ischemia, yet it can miss small lesions, especially when only axial slices are employed. This study investigated whether ischemic lesions visible in a single imaging plane correspond to smaller volumes and whether coronal DWI enhances detection compared to axial DWI alone. Methods: This retrospective single-center study examined 134 patients with brainstem ischemic strokes between December 2018 and November 2023. All patients underwent axial and coronal DWI. Clinical data, NIH Stroke Scale (NIHSS) scores, and modified Rankin Scale (mRS) scores were recorded. Diffusion-restricted lesion volumes were calculated using multiple models (planimetric, ellipsoid, and spherical), and lesion visibility per imaging plane was analyzed. Results: Brainstem ischemic strokes were detected in 85.8% of patients. Coronal DWI alone identified 6% of lesions that were undetectable on axial DWI; meanwhile, axial DWI alone identified 6.7%. Combining both improved overall sensitivity to 86.6%. Ischemic lesions visible in only one plane were significantly smaller across all volume models. Higher NIHSS scores were strongly correlated with larger diffusion-restricted lesion volumes. Coronal DWI correlated better with clinical severity than axial DWI, especially in the midbrain and medulla. Conclusions: Coronal DWI significantly improves the detection of small brainstem infarcts and should be incorporated into routine stroke imaging protocols. Infarcts visible in only one plane are typically smaller, yet still clinically relevant. Combined imaging enhances diagnostic accuracy and supports early and precise intervention in posterior circulation strokes. Full article