Clinical Neuroradiology最新文献

Purpose: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether Artificial intelligence (AI) can provide high-quality ICH diagnostics and turnaround times suitable for routine radiological practice.

Methods: A convolutional neural network (CNN) was trained and validated to detect ICHs on DICOM images of cranial CT (CCT) scans, utilizing about 674,000 individually labeled slices. The CNN was then incorporated into a commercial AI engine and seamlessly integrated into three pilot centers in Germany. A real-world test-dataset was extracted and manually annotated by two experienced experts. The performance of the AI algorithm against the two raters was assessed and compared to the inter-rater agreement. The overall time ranging from data acquisition to the delivery of the AI results was analyzed.

Results: Out of 6284 CCT examinations acquired in three different centers, 947 (15%) had ICH. Breakdowns of hemorrhage types included 8% intraparenchymal, 3% intraventricular, 6% subarachnoidal, 7% subdural, < 1% epidural hematomas. Comparing the AI's performance on a subset of 255 patients with two expert raters, it achieved a sensitivity of 0.90, a specificity of 0.96, an accuracy of 0.96. The corresponding inter-rater agreement was 0.84, 0.98, and 0.96. The overall median processing times for the three centers were 9, 11, and 12 min, respectively.

Conclusion: We showed that an AI algorithm for the automatic detection of ICHs can be seamlessly integrated into clinical workflows with minimal turnaround time. The accuracy was on par with radiology experts, making the system suitable for routine clinical use.

Purpose

Polymicrogyria (PMG) is a cortical malformation frequently associated with epilepsy. Our aim was to investigate the frequency and conspicuity of enlarged perivascular spaces (EPVS) underneath dysplastic cortex as a potentially underrecognized feature of PMG in pediatric and adolescent patients undergoing clinical magnetic resonance imaging (MRI).

Methods

We analyzed data from 28 pediatric and adolescent patients with PMG and a matched control group, ranging in age from 2 days to 21 years, who underwent MRI at 1.5T or 3T. T2-weighted MR images were examined for the presence of EPVS underneath the dysplastic cortex. The quantity of EPVS was graded from 0 to 4 (0: none, 1: < 10, 2: 11–20, 3: 21–40, 4: > 40 EPVS). We then compared the presence and quantity of EPVS to the matched controls in terms of total EPVS scores, and EPVS scores underneath the dysplastsic cortex depending on the age groups, the localization of PMG, and the MRI field strength.

Results

In 23/28 (82%) PMG patients, EPVS spatially related to the dysplastic cortex were identified. EPVS scores were significantly higher in PMG patients compared to controls, independent from age or PMG location. No significant differences were observed in EPVS scores in patients examined at 1.5T compared to those examined at 3T.

Conclusion

EPVS underneath the dysplastic cortex were identified in 82% of patients. EPVS may serve as an important clue for PMG and a marker for cortical malformation.

Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.

Purpose: Cerebral hemodynamics are important for the management of intracranial atherosclerotic stenosis (ICAS). The quantitative flow ratio (QFR) is a novel angiography-derived index for assessing the functional relevance of ICAS without pressure wires and adenosine. Good diagnostic yield with the hyperemic fractional flow reserve (FFR) have been reported, while data on the comparison of QFR to FFR are scarce.

Methods: In this prospective study 56 patients with anterior circulation symptomatic ICAS who received endovascular treatment were included. The new method of computing QFR from a single angiographic view, i.e., the Murray law-based QFR (μQFR), was applied to the examined vessels. An artificial intelligence algorithm was developed to realize the automatic delineation of vascular contour. Pressure gradients were measured before and after treatment within the lesion vessel using a pressure guidewire and the FFR was calculated.

Results: There was a good correlation between μQFR and FFR. Preoperative FFR predicted DWI watershed infarction (FFR optimal cut-off level: 0.755). Preoperative μQFR predicted DWI watershed infarction (μQFR optimal cut-off level: 0.51). Preoperative FFR predicted CTP hypoperfusion (FFR best predictive value: 0.62). Preoperative μQFR predicted CTP hypoperfusion (μQFR best predictive value: 0.375).

Conclusion: The μQFR based on DSA images can be used as an indicator to assess the functional status of the lesion in patients with ICAS.

Background and importance: Flow diverters (FDs) provide curative endovascular treatment for wide-necked sidewall aneurysms. The efficacy of FDs for bifurcation or branching sidewall aneurysms is probably limited. We used anatomical flow diversion (AFD) for intractable large cerebral aneurysms. We report our experiences with AFD.

Methods: The concept of AFD is the transformation from the bifurcation or branching sidewall type to the nonbranching sidewall type. Linearization of the parent artery by stenting, intentional branch occlusion, and aneurysmal coil embolization were performed. Furthermore, bypass surgery is performed for patients intolerant to branch occlusions. We evaluated the clinical outcomes of intractable aneurysms treated with AFD.

Results: AFD was performed in seven unruptured large aneurysms. Aneurysmal locations were the top of the basilar artery (BA), BA-superior cerebellar artery (SCA), internal carotid artery (IC)-posterior communicating artery (PcomA), and IC terminal. The mean dome diameter was 17.0 ± 4.6 mm. Six patients underwent bypass surgery. The occluded branches were the PCA + SCA, PcomA, and anterior cerebral artery (ACA) A1. An FD was used in three patients and a neck bridge stent in four patients. No intraprocedural complications occurred. Two postprocedural ischemic complications occurred in one patient. Six (86%) patients demonstrated a modified Rankin Scale (mRS) 0 at the 3-month follow-up, and one with an ischemic complication showed an mRS 5. Complete occlusion of all aneurysms was maintained with a median follow-up duration of 60 months.

Conclusion: AFD is useful for intractable large cerebral aneurysms with high curability, although safety verification is required.

Purpose: Artificial intelligence (AI) has emerged as a transformative force in medical research and is garnering increased attention in the public consciousness. This represents a critical time period in which medical researchers, healthcare providers, insurers, regulatory agencies, and patients are all developing and shaping their beliefs and policies regarding the use of AI in the healthcare sector. The successful deployment of AI will require support from all these groups. This commentary proposes that widespread support for medical AI must be driven by clear and transparent scientific reporting, beginning at the earliest stages of scientific research.

Methods: A review of relevant guidelines and literature describing how scientific reporting plays a central role at key stages in the life cycle of an AI software product was conducted. To contextualize this principle within a specific medical domain, we discuss the current state of predictive tissue outcome modeling in acute ischemic stroke and the unique challenges presented therein.

Results and conclusion: Translating AI methods from the research to the clinical domain is complicated by challenges related to model design and validation studies, medical product regulations, and healthcare providers' reservations regarding AI's efficacy and affordability. However, each of these limitations is also an opportunity for high-impact research that will help to accelerate the clinical adoption of state-of-the-art medical AI. In all cases, establishing and adhering to appropriate reporting standards is an important responsibility that is shared by all of the parties involved in the life cycle of a prospective AI software product.