During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, neurological symptoms increasingly moved into the focus of interest. In this prospective cohort study, we assessed neurological and cognitive symptoms in hospitalized coronavirus disease-19 (COVID-19) patients and aimed to determine their neuronal correlates. Patients with reverse transcription-PCR-confirmed COVID-19 infection who required inpatient treatment primarily because of non-neurological complications were screened between 20 April 2020 and 12 May 2020. Patients (age > 18 years) were included in our cohort when presenting with at least one new neurological symptom (defined as impaired gustation and/or olfaction, performance < 26 points on a Montreal Cognitive Assessment and/or pathological findings on clinical neurological examination). Patients with ≥2 new symptoms were eligible for further diagnostics using comprehensive neuropsychological tests, cerebral MRI and 18fluorodeoxyglucose (FDG) PET as soon as infectivity was no longer present. Exclusion criteria were: premorbid diagnosis of cognitive impairment, neurodegenerative diseases or intensive care unit treatment. Of 41 COVID-19 inpatients screened, 29 patients (65.2 ± 14.4 years; 38% female) in the subacute stage of disease were included in the register. Most frequently, gustation and olfaction were disturbed in 29/29 and 25/29 patients, respectively. Montreal Cognitive Assessment performance was impaired in 18/26 patients (mean score 21.8/30) with emphasis on frontoparietal cognitive functions. This was confirmed by detailed neuropsychological testing in 15 patients. 18FDG PET revealed pathological results in 10/15 patients with predominant frontoparietal hypometabolism. This pattern was confirmed by comparison with a control sample using voxel-wise principal components analysis, which showed a high correlation (R2 = 0.62) with the Montreal Cognitive Assessment performance. Post-mortem examination of one patient revealed white matter microglia activation but no signs of neuroinflammation. Neocortical dysfunction accompanied by cognitive decline was detected in a relevant fraction of patients with subacute COVID-19 initially requiring inpatient treatment. This is of major rehabilitative and socioeconomic relevance.

Background Radiological imaging guidelines are crucial for accurate diagnosis and optimal patient care as they result in standardized decisions and thus reduce inappropriate imaging studies. Purpose In the present study, we investigated the potential to support clinical decision-making using an interactive chatbot designed to provide personalized imaging recommendations from American College of Radiology (ACR) appropriateness criteria documents using semantic similarity processing. Methods We utilized 209 ACR appropriateness criteria documents as specialized knowledge base and employed LlamaIndex, a framework that allows to connect large language models with external data, and the ChatGPT 3.5-Turbo to create an appropriateness criteria contexted chatbot (accGPT). Fifty clinical case files were used to compare the accGPT's performance against general radiologists at varying experience levels and to generic ChatGPT 3.5 and 4.0. Results All chatbots reached at least human performance level. For the 50 case files, the accGPT performed best in providing correct recommendations that were "usually appropriate" according to the ACR criteria and also did provide the highest proportion of consistently correct answers in comparison with generic chatbots and radiologists. Further, the chatbots provided substantial time and cost savings, with an average decision time of 5 minutes and a cost of 0.19 € for all cases, compared to 50 minutes and 29.99 € for radiologists (both p < 0.01). Conclusion ChatGPT-based algorithms have the potential to substantially improve the decision-making for clinical imaging studies in accordance with ACR guidelines. Specifically, a context-based algorithm performed superior to its generic counterpart, demonstrating the value of tailoring AI solutions to specific healthcare applications.

Background Recently introduced photon-counting CT may improve noninvasive assessment of patients with high risk for coronary artery disease (CAD). Purpose To determine the diagnostic accuracy of ultrahigh-resolution (UHR) coronary CT angiography (CCTA) in the detection of CAD compared with the reference standard of invasive coronary angiography (ICA). Materials and Methods In this prospective study, participants with severe aortic valve stenosis and clinically indicated CT for transcatheter aortic valve replacement planning were consecutively enrolled from August 2022 to February 2023. All participants were examined with a dual-source photon-counting CT scanner using a retrospective electrocardiography-gated contrast-enhanced UHR scanning protocol (tube voltage, 120 or 140 kV; collimation, 120 × 0.2 mm; 100 mL of iopromid; no spectral information). Subjects underwent ICA as part of their clinical routine. A consensus assessment of image quality (five-point Likert scale: 1 = excellent [absence of artifacts], 5 = nondiagnostic [severe artifacts]) and a blinded independent reading for the presence of CAD (stenosis ≥50%) were performed. UHR CCTA was compared with ICA using area under the receiver operating characteristic curve (AUC). Results Among 68 participants (mean age, 81 years ± 7 [SD]; 32 male, 36 female), the prevalence of CAD and prior stent placement was 35% and 22%, respectively. The overall image quality was excellent (median score, 1.5 [IQR, 1.3-2.0]). The AUC of UHR CCTA in the detection of CAD was 0.93 per participant (95% CI: 0.86, 0.99), 0.94 per vessel (95% CI: 0.91, 0.98), and 0.92 per segment (95% CI: 0.87, 0.97). Sensitivity, specificity, and accuracy, respectively, were 96%, 84%, and 88% per participant (n = 68); 89%, 91%, and 91% per vessel (n = 204); and 77%, 95%, and 95% per segment (n = 965). Conclusion UHR photon-counting CCTA provided high diagnostic accuracy in the detection of CAD in a high-risk population, including subjects with severe coronary calcification or prior stent placement. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Williams and Newby in this issue.

TotalSegmentator MRI is an open-source, user-friendly tool that provides automatic, robust sequence-independent segmentations of major anatomic structures on MRI scans; the ready-to-use online tool is available at https://totalsegmentator.com .

After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition, marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with Post-COVID-Condition, healthy controls, and individuals that contracted COVID-19 without long-term symptoms. We reveal widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in close anatomical-functional relationship with the respective symptoms.

While neuropathological examinations in patients who died from COVID-19 revealed inflammatory changes in cerebral white matter, cerebral MRI frequently fails to detect abnormalities even in the presence of neurological symptoms. Application of multi-compartment diffusion microstructure imaging (DMI), that detects even small volume shifts between the compartments (intra-axonal, extra-axonal and free water/CSF) of a white matter model, is a promising approach to overcome this discrepancy. In this monocentric prospective study, a cohort of 20 COVID-19 inpatients (57.3 ± 17.1 years) with neurological symptoms (e.g. delirium, cranial nerve palsies) and cognitive impairments measured by the Montreal Cognitive Assessment (MoCA test; 22.4 ± 4.9; 70% below the cut-off value <26/30 points) underwent DMI in the subacute stage of the disease (29.3 ± 14.8 days after positive PCR). A comparison of whole-brain white matter DMI parameters with a matched healthy control group (n = 35) revealed a volume shift from the intra- and extra-axonal space into the free water fraction (V-CSF). This widespread COVID-related V-CSF increase affected the entire supratentorial white matter with maxima in frontal and parietal regions. Streamline-wise comparisons between COVID-19 patients and controls further revealed a network of most affected white matter fibres connecting widespread cortical regions in all cerebral lobes. The magnitude of these white matter changes (V-CSF) was associated with cognitive impairment measured by the MoCA test (r = −0.64, P = 0.006) but not with olfactory performance (r = 0.29, P = 0.12). Furthermore, a non-significant trend for an association between V-CSF and interleukin-6 emerged (r = 0.48, P = 0.068), a prominent marker of the COVID-19 related inflammatory response. In 14/20 patients who also received cerebral ¹⁸F-FDG PET, V-CSF increase was associated with the expression of the previously defined COVID-19-related metabolic spatial covariance pattern (r = 0.57; P = 0.039). In addition, the frontoparietal-dominant pattern of neocortical glucose hypometabolism matched well to the frontal and parietal focus of V-CSF increase. In summary, DMI in subacute COVID-19 patients revealed widespread volume shifts compatible with vasogenic oedema, affecting various supratentorial white matter tracts. These changes were associated with cognitive impairment and COVID-19 related changes in ¹⁸F-FDG PET imaging.