[A case of secondary immune thrombocytopenia induced by atezolizumab plus bevacizumab therapy for hepatocellular carcinoma resulting in death from intracranial hemorrhage].

An 84-year-old woman underwent laparoscopic partial hepatectomy for a single 3.0-cm-diameter nodule of hepatocellular carcinoma at segment 5. Although the postoperative condition was uneventful, multiple recurrences were observed six months after hepatic resection. Transcatheter arterial chemoembolization (TACE) was the second-line therapy. However, she was later diagnosed as TACE refractory due to residual tumor and presence of portal vein tumor thrombus. Third-line therapy was the combination of atezolizumab and bevacizumab, which was discontinued after 22 courses due to disease progression. Two months after the conclusion of chemotherapy, sudden onset of purpura was observed around her eyes and on her lower legs. Laboratory tests revealed severe thrombocytopenia, and she was diagnosed with secondary immune thrombocytopenic purpura. Steroids and immunoglobulin therapy were then administered. Although the immature platelet fraction increased after treatment, the platelet count did not. The patient died of intracranial hemorrhage 10 days after initiation of steroid and immunoglobulin therapy.

[The prevalence and risk factors of intracranial non-traumatic hemorrhage in newborns]. / Rasprostranennost' i faktory riska vnutricherepnogo netravmaticheskogo krovoizliyaniya u novorozhdennykh.

OBJECTIVE: To assess the prevalence rate of intracranial hemorrhage in newborns and to detect the size of the relative risk for different characteristics of the background of intrauterine development according to the materials of the Republican Prenatal Center. MATERIAL AND METHODS: The study was conducted on the basis of the Republican Prenatal Center of the Azerbaijan Republic. All newborns (996 children) (birth weight - 500 grams or more, gestational age - over 22 completed weeks) have been examined by a neuropathologist. If there were signs of any neurological abnormalities, an ultrasound examination of the brain was performed. RESULTS: The prevalence rate of intracranial non-traumatic hemorrhage (INTH) among newborns was 15.66% (4.8% among full-term, 48.8% among premature). Depending on mother's age the prevalence rate of INTH changed from 12.7% (mother's age 20-24 years) to 23.0% (mother's age 35 years or more). CONCLUSION: The risk of INTH statistically significantly increased with maternal age 35 years or more, with 4 or more births, with the birth of a fetus weighing up to 2500 and more than 4000 grams, with a gestational age of less than 37 weeks, with multiple pregnancies and with vaginal delivery.

External validation of different predictive scores for symptomatic intracranial hemorrhage after intravenous thrombolysis in Asian stroke patients.

OBJECTIVE: This study aimed to externally validate different predictive scores for symptomatic intracranial hemorrhage (SICH) after intravenous thrombolysis (IVT), with a particular focus on their predictive abilities in Asian stroke patients. METHODS: We retrospectively enrolled stroke patients who received a standard dose of alteplase within 4.5 hours from symptom onset at the First Affiliated Hospital of Dalian Medical University from July 2010 to August 2023. SICH was defined as the hemorrhagic transformation detected on the head CT scan completed within 48 h post-IVT, accompanied by a clinical deterioration of at least a 4-point increase in NIHSS score. Predictive abilities of the HAT, MSS, SEDAN, SPAN-100, and GRASPS scores were tested. Discrimination and calibration were performed using the area under the receiver operating characteristic curve (ROC-AUC), DeLong test, and Hosmer-Lemeshow (H-L) goodness-of-fit test. RESULTS: The study included 1007 stroke patients, of whom 31 (3.08 %) developed SICH. ROC-AUCs for predicting SICH were: 0.796 (95 %CI: 0.726-0.866) for the GRASPS score, 0.724 (95 %CI: 0.644-0.804) for the MSS score, 0.715 (95 %CI: 0.619-0.811) for the SEDAN score, 0.714 (95 %CI: 0.611-0.817) for the HAT score, and 0.605 (95 %CI: 0.491-0.720) for the SPAN-100 score (all P < 0.05). DeLong tests showed that the GRASPS score demonstrated significantly better discrimination than the MSS score (P = 0.010), the SEDAN score (P = 0.009), the HAT score (P = 0.049), and the SPAN-100 score (P = 0.000). H-L tests indicated good calibrations which were ranked HAT > SEDAN > MSS > SPAN-100 > GRASPS scores. CONCLUSION: The GRASPS score showed reasonable predictive ability for SICH, indicating its potential utility for Asian stroke patients receiving IVT.

Radiomics of intrathrombus and perithrombus regions for Post-EVT intracranial hemorrhage risk Prediction: A multicenter CT study.

OBJECTIVES: This study aimed to assess the predictive performance of radiomics derived from computed tomography (CT) images of thrombus regions in predicting the risk of intracranial hemorrhage (ICH) following endovascular thrombectomy (EVT). MATERIALS AND METHODS: This retrospective multicenter study included 336 patients who underwent admission CT and EVT for acute anterior-circulation large vessel occlusion between December 2018 and December 2023. Follow-up imaging was performed 24 h post-procedure to evaluate the occurrence of ICH. 230 patients from centers A and B were randomly allocated into training and test groups in a 7:3 ratio, while the remaining 106 patients from center C comprised the validation cohort. Radiologists manually segmenting the thrombus on CT images, and the perithrombus region was defined by expanding the initial region of interest (ROI). A total of 428 radiomics features were extracted from both intrathrombus and perithrombus regions on CT images. The Mann-Whitney U test was used for feature selection, and least absolute shrinkage and selection operator (LASSO) regression was employed for model development, followed by validation using a 5-fold cross-validation approach. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). RESULTS: Among the eligible patients, 128 (38.1 %) experienced ICH after EVT. The combined model exhibited superior performance in the training cohort (AUC: 0.913, 95 % CI: 0.861-0.965), test cohort (AUC: 0.868, 95 % CI: 0.775-0.962), and validation cohort (AUC: 0.850, 95 % CI: 0.768-0.912). Notably, in the validation group, both the perithrombus and combined models demonstrated higher predictive accuracy compared to the intrathrombus model (0.837 vs. 0.684, p = 0.02; AUC: 0.850 vs. 0.684, p = 0.01). CONCLUSIONS: Radiomics features derived from the perithrombus region significantly enhance the prediction of ICH after EVT, providing valuable insights for optimizing post-procedural clinical decisions. CLINICAL RELEVANCE STATEMENT: This study highlights the importance of radiomics extracted from intrathrombus and perithrombus region in predicting intracranial hemorrhagefollowing endovascular thrombectomy, which can aid in improving patient outcomes.

Vascular imaging for Stereoelectroencephalography: A safety and planning study.

BACKGROUND: Stereoelectroencephalography (SEEG) is a procedure used to localize the epileptogenic zone in patients with medically refractory epilepsy, involving the stereotactic implantation of electrodes into brain parenchyma. Magnetic Resonance Imaging (MRI), Digital Subtraction Angiography, and Computed Tomography have been used preoperatively to prevent Intracranial Hemorrhage (ICH) by identifying electrode-vessel conflicts (EVC's) on planned electrode trajectories. There is variation in the use of Digital Subtraction Angiography and non-invasive sequences for vascular planning. Digital Subtraction Angiography provides high spatial resolution, but carries risks of arterial dissection, groin and retroperitoneal hematoma, and a 0.5-1.9% risk of stroke. Our group has incorporated Intravenous Cone Beam Computed Tomography (CBCT A/V) Brain into our SEEG workflow, given its effective implementation in other neurosurgical domains. Primary aims include validating the safety of our CBCT A/V sequence for SEEG planning and determining if CBCT A/V is comparable to other modalities in detecting EVC's. Secondary aims include elucidating the relationship of conflicting vessel calibre with ICH incidence in SEEG using CBCT A/V imaging. METHODS: A single-center retrospective study was conducted of 20 patients who underwent preoperative CBCT A/V Brain and MRI Brain with gadolinium enhancement, encompassing 273 electrode implantations from August 2020 - July 2023. The incidence and grade of post-implant, post-explant symptomatic ICH and asymptomatic ICH was noted. The total number of EVC's identifiable on MRI and CBCT A/V was recorded, along with average diameter of conflicting vessels. RESULTS: Across 20 patients and 273 implanted electrodes, there were four ICH events, where two were symptomatic and two were asymptomatic. The mean diameter of EVC's across all patients was 1.4 mm (±0.5). A significant difference (P < 0.0001) was observed between the number of EVC's that CBCT A/V could identify (20) compared to MRI (6). Two EVC's were identified in the region of two symptomatic ICH's, with the mean diameter of these conflicted vessels being 1.5 mm (±0.4). The two symptomatic ICH-associated EVC's were observed on CBCT A/V but not MRI. CONCLUSIONS: In our series, CBCT A/V demonstrates an acceptable safety profile for SEEG planning compared to other imaging modalities. CBCT A/V identified significantly more EVC's compared to MRI, including those contributing to transient symptomatic intracranial hemorrhage. A conflicting vessel calibre of less than 1.2 mm on CBCT A/V did not contribute to ICH in our SEEG series.

Deep Learning to Detect Intracranial Hemorrhage in a National Teleradiology Program and the Impact on Interpretation Time.

The diagnostic performance of an artificial intelligence (AI) clinical decision support solution for acute intracranial hemorrhage (ICH) detection was assessed in a large teleradiology practice. The impact on radiologist read times and system efficiency was also quantified. A total of 61 704 consecutive noncontrast head CT examinations were retrospectively evaluated. System performance was calculated along with mean and median read times for CT studies obtained before (baseline, pre-AI period; August 2021 to May 2022) and after (post-AI period; January 2023 to February 2024) AI implementation. The AI solution had a sensitivity of 75.6%, specificity of 92.1%, accuracy of 91.7%, prevalence of 2.70%, and positive predictive value of 21.1%. Of the 56 745 post-AI CT scans with no bleed identified by a radiologist, examinations falsely flagged as suspected ICH by the AI solution (n = 4464) took an average of 9 minutes 40 seconds (median, 8 minutes 7 seconds) to interpret as compared with 8 minutes 25 seconds (median, 6 minutes 48 seconds) for unremarkable CT scans before AI (n = 49 007) (P < .001) and 8 minutes 38 seconds (median, 6 minutes 53 seconds) after AI when ICH was not suspected by the AI solution (n = 52 281) (P < .001). CT scans with no bleed identified by the AI but reported as positive for ICH by the radiologist (n = 384) took an average of 14 minutes 23 seconds (median, 13 minutes 35 seconds) to interpret as compared with 13 minutes 34 seconds (median, 12 minutes 30 seconds) for CT scans correctly reported as a bleed by the AI (n = 1192) (P = .04). With lengthened read times for falsely flagged examinations, system inefficiencies may outweigh the potential benefits of using the tool in a high volume, low prevalence environment. Keywords: Artificial Intelligence, Intracranial Hemorrhage, Read Time, Report Turnaround Time, System Efficiency Supplemental material is available for this article. © RSNA, 2024.

18FFlutemetamol-PET Aided Classification of Cerebral Amyloid Angiopathy: A Multicenter Study.

OBJECTIVES: Cerebral amyloid angiopathy (CAA)-related features on neuroimaging often coexist with signs of arteriolosclerosis-small vessel disease on neuroimaging in people with intracranial hemorrhage (ICH). This study aimed at defining the value of amyloid pathology detected by 18Fflutemetamol PET in reclassification and stratification of risk of bleeding in people with mixed CAA-arteriolosclerosis features. METHODS: We included consecutive patients admitted to 2 institutions (2018-2023) with spontaneous symptomatic ICH, subarachnoid hemorrhage (SAH), transient focal neurologic episodes (TFNE), or cognitive impairment and MRI showing CAA hallmarks. All patients underwent brain magnetic resonance imaging (MRI) with susceptibility weighted imaging and 18Fflutemetamol PET imaging and were followed up for at least 1 year. We compared cases with CAA and arteriolosclerosis + CAA features and defined long-term outcomes (composite outcome including death, ICH, ischemic stroke, SAH, TFNE) depending on PET status (CAA/amyloid pathology vs arteriolosclerosis-predominant groups). RESULTS: Among 47 patients, according to PET and MRI imaging, 38 patients were reclassified in the CAA/amyloid pathology group and 9 in the arteriolosclerosis-predominant group, with similar cardiovascular risk factors but a significantly higher lobar microbleed burden for the former group. The CAA/amyloid pathology group had higher rates of composite outcome (43.9 vs 11.1 events per 100 patient-year; p = 0.039) and ICH (36.5 vs 5.6 events per 100 patient-years; p = 0.04) compared with the arteriolosclerosis-predominant group. DISCUSSION: 18FFlutemetamol PET imaging can help in reclassification of mixed arteriolosclerosis + CAA into CAA/amyloid pathology and arteriolosclerosis-predominant, with implications on long-term risk of recurrent events. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that 18Fflutemetamol PET can distinguish between CAA + arteriolosclerosis and arteriolosclerosis-predominant pathology.

Neonatal intracranial haemorrhage secondary to vein of Galen aneurysmal dilatation.

Paediatric neurovascular anomalies associated with the vein of Galen (VG) comprise of a spectrum of rare, complex, and life-threatening conditions. In this group, the "vein of Galen aneurysmal dilatation" (VGAD) is a distinct entity that often presents with progressive neurological symptoms in older children. Acute haemorrhage in VGAD is uncommon. We present an unusual presentation of VGAD in a neonate and discuss the challenges faced in the management.

Diagnostic test accuracy of externally validated convolutional neural network (CNN) artificial intelligence (AI) models for emergency head CT scans - A systematic review.

BACKGROUND: The surge in emergency head CT imaging and artificial intelligence (AI) advancements, especially deep learning (DL) and convolutional neural networks (CNN), have accelerated the development of computer-aided diagnosis (CADx) for emergency imaging. External validation assesses model generalizability, providing preliminary evidence of clinical potential. OBJECTIVES: This study systematically reviews externally validated CNN-CADx models for emergency head CT scans, critically appraises diagnostic test accuracy (DTA), and assesses adherence to reporting guidelines. METHODS: Studies comparing CNN-CADx model performance to reference standard were eligible. The review was registered in PROSPERO (CRD42023411641) and conducted on Medline, Embase, EBM-Reviews and Web of Science following PRISMA-DTA guideline. DTA reporting were systematically extracted and appraised using standardised checklists (STARD, CHARMS, CLAIM, TRIPOD, PROBAST, QUADAS-2). RESULTS: Six of 5636 identified studies were eligible. The common target condition was intracranial haemorrhage (ICH), and intended workflow roles auxiliary to experts. Due to methodological and clinical between-study variation, meta-analysis was inappropriate. The scan-level sensitivity exceeded 90 % in 5/6 studies, while specificities ranged from 58,0-97,7 %. The SROC 95 % predictive region was markedly broader than the confidence region, ranging above 50 % sensitivity and 20 % specificity. All studies had unclear or high risk of bias and concern for applicability (QUADAS-2, PROBAST), and reporting adherence was below 50 % in 20 of 32 TRIPOD items. CONCLUSION: 0.01 % of identified studies met the eligibility criteria. The evidence on the DTA of CNN-CADx models for emergency head CT scans remains limited in the scope of this review, as the reviewed studies were scarce, inapt for meta-analysis and undermined by inadequate methodological conduct and reporting. Properly conducted, external validation remains preliminary for evaluating the clinical potential of AI-CADx models, but prospective and pragmatic clinical validation in comparative trials remains most crucial. In conclusion, future AI-CADx research processes should be methodologically standardized and reported in a clinically meaningful way to avoid research waste.

Nomogram established on account of Lasso-logistic regression for predicting hemorrhagic transformation in patients with acute ischemic stroke after endovascular thrombectomy.

BACKGROUND: Hemorrhagic transformation (HT) is a common and serious complication in patients with acute ischemic stroke (AIS) after endovascular thrombectomy (EVT). This study was performed to determine the predictive factors associated with HT in stroke patients with EVT and to establish and validate a nomogram that combines with independent predictors to predict the probability of HT after EVT in patients with AIS. METHODS: All patients were randomly divided into development and validation cohorts at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal factors, and multivariate logistic regression analysis was used to build a clinical prediction model. Calibration plots, decision curve analysis (DCA) and receiver operating characteristic curve (ROC) were generated to assess predictive performance. RESULTS: LASSO regression analysis showed that Alberta Stroke Program Early CT Scores (ASPECTS), international normalized ratio (INR), uric acid (UA), neutrophils (NEU) were the influencing factors for AIS with HT after EVT. A novel prognostic nomogram model was established to predict the possibility of HT with AIS after EVT. The calibration curve showed that the model had good consistency. The results of ROC analysis showed that the AUC of the prediction model established in this study for predicting HT was 0.797 in the development cohort and 0.786 in the validation cohort. CONCLUSION: This study proposes a novel and practical nomogram based on ASPECTS, INR, UA, NEU, which can well predict the probability of HT after EVT in patients with AIS.

Incidence and clinical analysis of asymptomatic intracranial hemorrhage in neonates with cerebral hypoxic-ischaemic risk based on multisequence MR images.

The incidence and clinical distribution of intracranial haemorrhage (ICH) in neonates at risk of cerebral hypoxia-ischaemia have not been reported in specific studies. Based on conventional magnetic resonance imaging (MRI) versus susceptibility weighted imaging (SWI), this study aimed to analyse the occurrence of asymptomatic ICH in newborns with or without risk of cerebral hypoxia-ischaemia and to accumulate objective data for clinical evaluations of high-risk neonates and corresponding response strategies. 317 newborns were included. MRI revealed that the overall incidence of ICH was 59.31%. The most common subtype was intracranial extracerebral haemorrhage (ICECH) which included subarachnoid haemorrhage (SAH) and subdural haemorrhage (SDH). ICECH accounted for 92.02% of ICH. The positive detection rate of ICECH by SWI was significantly higher than that by T1WI. The incidence of total ICH, ICECH and SAH was greater among children who were delivered vaginally than among those who underwent caesarean delivery. Asymptomatic neonatal ICH may be a common complication of the neonatal birth process, and SWI may improve the detection rate. Transvaginal delivery and a weight greater than 2500 g were associated with a high incidence of ICECH in neonates. The impact of neonatal cerebral hypoxia-ischaemia risk factors on the occurrence of asymptomatic ICH may be negligible.

Cerebellar mutism syndrome caused by bilateral cerebellar hemorrhage in adults: a case report and review of the literature.

Cerebellar mutism syndrome (CMS) is a frequent complication of surgical intervention on posterior fossa in children. It has been only occasionally reported in adults and its features have not been fully characterized. In children and in young adults, medulloblastoma is the main reason for neurosurgery. A single case of postsurgical CMS is presented in an adult patient with a cerebellar hemorrhage and a systematic review of the published individual cases of CMS in adults was done. Literature review of individual cases found 30 patients, 18/30 (60%) males, from 20 to 71 years at diagnosis. All but one case was post-surgical, but in one of the post-surgical cases iatrogenic basilar artery occlusion was proposed as cause for CMS. The causes were: primary tumors of the posterior fossa in 16/22 (72.7%) metastasis in 3/30 (10%), ischemia in 3/30 (10%) cerebellar hemorrhage in 3/30 (10%), and benign lesions in 2/30 (6.7%) patients. 8/30 patients (26.7%) were reported as having persistent or incomplete resolution of CMS within 12 months. CMS is a rare occurrence in adults and spontaneous cerebellar hemorrhage has been reported in 3/30 (10%) adult patients. The generally accepted hypothesis is that CMS results from bilateral damage to the dentate nucleus or the dentate-rubro-thalamic tract, leading to cerebro-cerebellar diaschisis. Several causes might contribute in adults. The prognosis of CMS is slightly worse in adults than in children, but two thirds of cases show a complete resolution within 6 months.

Improving Automated Hemorrhage Detection at Sparse-View CT via U-Net-based Artifact Reduction.

Purpose To explore the potential benefits of deep learning-based artifact reduction in sparse-view cranial CT scans and its impact on automated hemorrhage detection. Materials and Methods In this retrospective study, a U-Net was trained for artifact reduction on simulated sparse-view cranial CT scans in 3000 patients, obtained from a public dataset and reconstructed with varying sparse-view levels. Additionally, EfficientNet-B2 was trained on full-view CT data from 17 545 patients for automated hemorrhage detection. Detection performance was evaluated using the area under the receiver operating characteristic curve (AUC), with differences assessed using the DeLong test, along with confusion matrices. A total variation (TV) postprocessing approach, commonly applied to sparse-view CT, served as the basis for comparison. A Bonferroni-corrected significance level of .001/6 = .00017 was used to accommodate for multiple hypotheses testing. Results Images with U-Net postprocessing were better than unprocessed and TV-processed images with respect to image quality and automated hemorrhage detection. With U-Net postprocessing, the number of views could be reduced from 4096 (AUC: 0.97 [95% CI: 0.97, 0.98]) to 512 (0.97 [95% CI: 0.97, 0.98], P < .00017) and to 256 views (0.97 [95% CI: 0.96, 0.97], P < .00017) with a minimal decrease in hemorrhage detection performance. This was accompanied by mean structural similarity index measure increases of 0.0210 (95% CI: 0.0210, 0.0211) and 0.0560 (95% CI: 0.0559, 0.0560) relative to unprocessed images. Conclusion U-Net-based artifact reduction substantially enhanced automated hemorrhage detection in sparse-view cranial CT scans. Keywords: CT, Head/Neck, Hemorrhage, Diagnosis, Supervised Learning Supplemental material is available for this article. © RSNA, 2024.

Consideration of Brain CT Imaging Standard for Mild Head Injuries.

It has been reported that various clinical criteria indicate computed tomography (CT) examination for mild head injury (MHI). However, the decision to perform CT for MHI largely depends on the physician. Data on severe head injuries is available in sources such as the Japan Neurotrauma Data Bank, but only a few data has been collected on MHI. A total of 1688 patients with MHI (Glasgow Coma Scale 14 and 15) treated at our hospital from June 2017 to May 2019 were reviewed. CT was performed in 1237 patients (73.28%), and intracranial hemorrhage was detected in 50 patients. Three patients deteriorated, and all were surgically treated. Statistical analysis of the presence or absence of acute intracranial hemorrhage and "risk factors for complications of intracranial lesions in MHI" showed significant differences in unclear or ambiguous accident history (p = 0.022), continued post-traumatic amnesia (p < 0.01), trauma above the clavicles including clinical signs of skull fracture (skull base or depressed skull fracture) (p = 0.012), age <60 years (p < 0.01), coagulation disorders (p < 0.01), and alcohol or drug intoxication (p < 0.01). The 453 patients who did not satisfy these risk factors included only one patient with intracranial hemorrhage, so the negative predictive value was 99.78%. This study shows that the "risk factors for complications of intracranial lesions in MHI" are effective criteria for excluding acute intracranial hemorrhage and CT should be actively considered for patients with the above factors that showed significant differences.

An efficient deep neural network for automatic classification of acute intracranial hemorrhages in brain CT scans.

BACKGROUND: Recent advancements in deep learning models have demonstrated their potential in the field of medical imaging, achieving remarkable performance surpassing human capabilities in tasks such as classification and segmentation. However, these modern state-of-the-art network architectures often demand substantial computational resources, which limits their practical application in resource-constrained settings. This study aims to propose an efficient diagnostic deep learning model specifically designed for the classification of intracranial hemorrhage in brain CT scans. METHOD: Our proposed model utilizes a combination of depthwise separable convolutions and a multi-receptive field mechanism to achieve a trade-off between performance and computational efficiency. The model was trained on RSNA datasets and validated on CQ500 dataset and PhysioNet dataset. RESULT: Through a comprehensive comparison with state-of-the-art models, our model achieves an average AUROC score of 0.952 on RSNA datasets and exhibits robust generalization capabilities, comparable to SE-ResNeXt, across other open datasets. Furthermore, the parameter count of our model is just 3 % of that of MobileNet V3. CONCLUSION: This study presents a diagnostic deep-learning model that is optimized for classifying intracranial hemorrhages in brain CT scans. The efficient characteristics make our proposed model highly promising for broader applications in medical settings.

Incidence of intracranial bleeding in mild traumatic brain injury patients taking oral anticoagulants: a systematic review and meta-analysis.

BACKGROUND: Traumatic brain injury (TBI) is one of the leading causes of disability and death worldwide. Most TBI cases occur in older people, because they are at a higher risk of accidental falling. As the population ages, the use of anticoagulants is increasing. Some serious complications of TBI, such as intracranial hemorrhage (ICH), may occur even in mild cases. According to the current guidelines regarding managing mild TBI patients, a CT head scan is recommended for all patients receiving anticoagulation. We aim to assess the incidence of ICH in patients with mild TBI taking oral anticoagulants. METHODS: Our systematic review and meta-analysis were performed using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. The protocol was registered in PROSPERO (CRD42024503086). Twenty-eight studies evaluating patients with a mild TBI from ten countries with a total sample size of 11,172, 5671 on DOACs, and 5501 on VKAs were included in our meta-analysis. RESULTS: The random-effects overall incidence of ICH among oral anticoagulated patients with mild TBI was calculated to be 9.4% [95% CI 7.2-12.1%, I2 = 89%]. The rates of immediate ICH for patients taking DOACs and VKAs were 6.4% and 10.5%, respectively. The overall rate of immediate ICH in anticoagulated mild TBI patients was 8.5% [95% CI 6.6-10.9%], with a high heterogeneity between studies (I2 = 88%). Furthermore, the rates of delayed ICH in patients with mild TBI taking DOACs and VKAs were 1.6% and 1.9%, respectively. The overall incidence of delayed ICH among oral anticoagulated mild TBI patients was 1.7% [95% CI 1-2.8%, I2 = 79%]. The overall rate of ICH among mild TBI patients taking DOAC was calculated to be 7.3% [95% CI 5.2-10.3%], with significant heterogeneity between studies (I2 = 79%). However, the overall ICH rate is higher in patients who take only VKAs 11.3% [95% CI 8.6-14.7%, I2 = 83%]. Patients on DOACs were at lower risk of ICH after mild TBI compared to patients on VKAs (OR = 0.64, 95% CI 0.48-0.86, p < 0.01, I2 = 28%). CONCLUSION: Our meta-analysis confirms the need for performing brain CT scan in patients with mild TBI patients who receive oral anticoagulants before injury. Due to limited data, further multi-center, prospective studies are warranted to confirm the true incidence of traumatic ICH in patients on anticoagulants.