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Objectives To develop and validate an automatic classification algorithm for diagnosing Alzheimer’s disease (AD) or mild cognitive impairment (MCI). Methods and materials This study evaluated a high-performance interpretable network algorithm (TabNet) and compared its performance with that of XGBoost, a widely used classifier. Brain segmentation was performed using a commercially approved software. TabNet and XGBoost were trained on the volumes or radiomics features of 102 segmented regions for classifying subjects into AD, MCI, or cognitively normal (CN) groups. The diagnostic performances of the two algorithms were compared using areas under the curves (AUCs). Additionally, 20 deep learning–based AD signature areas were investigated. Results Between December 2014 and March 2017, 161 AD, 153 MCI, and 306 CN cases were enrolled. Another 120 AD, 90 MCI, and 141 CN cases were included for the internal validation. Public datasets were used for external validation. TabNet with volume features had an AUC of 0.951 (95% confidence interval [CI], 0.947–0.955) for AD vs CN, which was similar to that of XGBoost (0.953 [95% CI, 0.951–0.955], p  = 0.41). External validation revealed the similar performances of two classifiers using volume features (0.871 vs. 0.871, p  = 0.86). Likewise, two algorithms showed similar performances with one another in classifying MCI. The addition of radiomics data did not improve the performance of TabNet. TabNet and XGBoost focused on the same 13/20 regions of interest, including the hippocampus, inferior lateral ventricle, and entorhinal cortex. Conclusions TabNet shows high performance in AD classification and detailed interpretation of the selected regions. Clinical relevance statement Using a high-performance interpretable deep learning network, the automatic classification algorithm assisted in accurate Alzheimer’s disease detection using 3D T1-weighted brain MRI and detailed interpretation of the selected regions. Key Points • MR volumetry data revealed that TabNet had a high diagnostic performance in differentiating Alzheimer’s disease (AD) from cognitive normal cases, which was comparable with that of XGBoost. • The addition of radiomics data to the volume data did not improve the diagnostic performance of TabNet. • Both TabNet and XGBoost selected the clinically meaningful regions of interest in AD, including the hippocampus, inferior lateral ventricle, and entorhinal cortex.

This study aims to analyse the volumetric changes in brain MRI after cochlear implantation (CI), focusing on the speech perception in postlingually deaf adults. We conducted a prospective cohort study with 16 patients who had bilateral hearing loss and received unilateral CI. Based on the surgical side, patients were categorized into left and right CI groups. Volumetric T1-weighted brain MRI were obtained before and one year after the surgery. To overcome the artifact caused by the internal device in post-CI scan, image reconstruction method was newly devised and applied using the contralateral hemisphere of the pre-CI MRI data, to run FreeSurfer. We conducted within-subject template estimation for unbiased longitudinal image analysis, based on the linear mixed effect models. When analyzing the contralateral cerebral hemisphere before and after CI, a substantial increase in superior frontal gyrus and superior temporal gyrus (STG) volumes was observed in the left CI group. A positive correlation was observed in the STG and post-CI word recognition score in both groups. As far as we know, this is the first study attempting longitudinal brain volumetry based on post-CI MRI scans. We demonstrate that better auditory performance after CI is associated with structural restoration in central auditory structures.

To evaluate the adherence of large language model (LLM)-based healthcare research to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) checklist, a framework designed to enhance the transparency and reproducibility of studies on the accuracy of LLMs for medical applications. A systematic PubMed search was conducted to identify articles on LLM performance published in high-ranking clinical medicine journals (the top 10% in each of the 59 specialties according to the 2023 Journal Impact Factor) from November 30, 2022, through June 25, 2024. Data on the six MI-CLEAR-LLM checklist items: 1) identification and specification of the LLM used, 2) stochasticity handling, 3) prompt wording and syntax, 4) prompt structuring, 5) prompt testing and optimization, and 6) independence of the test data-were independently extracted by two reviewers, and adherence was calculated for each item. Of 159 studies, 100% (159/159) reported the name of the LLM, 96.9% (154/159) reported the version, and 91.8% (146/159) reported the manufacturer. However, only 54.1% (86/159) reported the training data cutoff date, 6.3% (10/159) documented access to web-based information, and 50.9% (81/159) provided the date of the query attempts. Clear documentation regarding stochasticity management was provided in 15.1% (24/159) of the studies. Regarding prompt details, 49.1% (78/159) provided exact prompt wording and syntax but only 34.0% (54/159) documented prompt-structuring practices. While 46.5% (74/159) of the studies detailed prompt testing, only 15.7% (25/159) explained the rationale for specific word choices. Test data independence was reported for only 13.2% (21/159) of the studies, and 56.6% (43/76) provided URLs for internet-sourced test data. Although basic LLM identification details were relatively well reported, other key aspects, including stochasticity, prompts, and test data, were frequently underreported. Enhancing adherence to the MI-CLEAR-LLM checklist will allow LLM research to achieve greater transparency and will foster more credible and reliable future studies.

This study assesses the diagnostic performance of six LLMs —GPT-4v, GPT-4o, Gemini 1.5 Pro, Gemini 1.5 Flash, Claude 3.0, and Claude 3.5—on complex neurology cases from JAMA Neurology and JAMA , focusing on their image interpretation abilities. We selected 56 radiology cases from JAMA Neurology and JAMA (from May 2015 to April 2024), rephrasing the text and reshuffling multiple-choice answer. Each LLM processed four input types: original quiz with images, rephrased text with images, rephrased text only, and images only. Model performance was compared with three neuroradiologists, and consistency was assessed across five repetitions using Fleiss’ kappa. In the image-only condition, LLMs answered six specific questions regarding modality, sequence, contrast, plane, anatomical, and pathologic locations, and their accuracy was evaluated. Claude 3.5 achieved the highest accuracy (80.4%) on original image and text inputs. The accuracy using the rephrased quiz text with image ranged from 62.5% (35/56) to 76.8% (43/56). The accuracy using the rephrased quiz text only ranged from 51.8% (29/56) to 76.8% (43/56). LLMs performed on par with first-year fellows (71.4% [40/56]) but surpassed junior faculty (51.8% [29/56]) and second-year fellows (48.2% [27/56]). All LLMs showed almost similar results across the five repetitions (0.860-1.000). In image-only tasks, LLM accuracy in identifying pathologic locations ranged from 21.5% (28/130) to 63.1% (82/130). LLMs exhibit strong diagnostic performance with clinical text, yet their ability to interpret complex radiologic images independently is limited. Further refinement in image analysis is essential for these models to integrate fully into radiologic workflows.

Aryl hydrocarbon receptor (AhR) antagonism can mitigate cellular damage associated with cerebral ischaemia and reperfusion (I/R) injury. This study investigated the neuroprotective effects of AhR antagonist administration before reperfusion in a rat stroke model and influence of the timing of AhR antagonist administration on its neuroprotective effects. Magnetic resonance imaging (MRI) was performed at baseline, immediately after, and 3, 8, and 24 h after ischaemia in the sham, control (I/R injury), TMF10 (trimethoxyflavone [TMF] administered 10 min post-ischaemia), and TMF50 (TMF administered 50 min post-ischaemia) groups. The TMF treatment groups had significantly fewer infarcts than the control group. At 24 h, the relative apparent diffusion coefficient values of the ischaemic core and peri-infarct region were significantly higher and relative T2 values were significantly lower in the TMF10 groups than in the control group. The TMF treatment groups showed significantly fewer terminal deoxynucleotidyl transferase dUTP nick-end labelling positive (+) cells (%) in the peri-infarct region than the control group. This study demonstrated that TMF treatment 10 or 50 min after ischaemia alleviated brain damage. Furthermore, the timing of AhR antagonist administration affected the inhibition of cellular or vasogenic oedema formation caused by a transient ischaemic stroke.

Distinguishing between Parkinson’s disease (PD) and essential tremor (ET) can be challenging sometimes. Although positron emission tomography can confirm PD diagnosis, its application is limited by high cost and exposure to radioactive isotopes. Patients with PD exhibit loss of the dorsal nigral hyperintensity on brain magnetic resonance imaging (MRI). Novel MRI-based approaches, including susceptibility map-weighted imaging (SMwI), allow visualization of the dorsal nigral hyperintensity at an increased resolution. Herein, we investigated the diagnostic accuracy of dorsal nigral hyperintensity evaluation on SMwI for distinguishing tremor-dominant PD from ET. Consecutive patients with tremor who underwent SMwI and were diagnosed with tremor-dominant PD or ET between July 2021 and July 2022 were enrolled. The dorsal nigral hyperintensity loss on SMwI was compared between the PD and ET groups. All 143 patients (100%) with tremor-dominant PD showed unilateral or bilateral dorsal nigral hyperintensity loss. Among 136 patients with ET, 131 (96.3%) exhibited an intact dorsal nigral hyperintensity, while 5 (3.7%) showed unilateral/bilateral dorsal nigral hyperintensity loss. SMwI discriminated between tremor-dominant PD and ET with a sensitivity and specificity of 100% and 96.3%, respectively. 18 F-FP-CIT PET revealed normal findings in 4/5 patients with ET who had false-positive results on SMwI. These results indicate that dorsal nigral hyperintensity loss on SMwI could differentiate between tremor-dominant PD and ET with high accuracy.

To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017-March 2018, n = 596) and internal validation test set (April 2018-June 2018, n = 204). Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm 3 ) ( P  < 0.001). The deep gray matter lesion volumes were significantly higher for positive than for negative involvement of the deep gray matter (5.6 ± 4.6 mm 3 vs. 1.0 ± 1.3 mm 3 , P  < 0.001). The mean Dice similarity coefficients were 0.90 and 0.94 for cortical and deep gray matter lesions, respectively. However, the volume of disease extent was not associated with worse overall survival (cortical extent: P  = 0.07; deep gray matter extent: P  = 0.12).

We aimed to investigate the detection rate of brain MR and MR angiography for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients with/without neurological symptoms. This retrospective study included consecutive patients with definite or possible left-sided infective endocarditis according to the modified Duke criteria who underwent brain MRI and MR angiography between March 2015 and October 2020. The detection rate for neuroimaging abnormality on MRI was defined as the number of patients with positive brain MRI findings divided by the number of patients with left-sided infective endocarditis. Positive imaging findings included acute ischemic lesions, cerebral microbleeds, hemorrhagic lesions, and infectious aneurysms. In addition, aneurysm rupture rate and median period to aneurysm rupture were evaluated on follow-up studies. A total 115 patients (mean age: 55 years ± 19; 65 men) were included. The detection rate for neuroimaging abnormality was 77% (89/115). The detection rate in patients without neurological symptoms was 70% (56/80). Acute ischemic lesions, cerebral microbleeds, and hemorrhagic lesions including superficial siderosis and intracranial hemorrhage were detected on MRI in 56% (64/115), 57% (66/115), and 20% (23/115) of patients, respectively. In particular, infectious aneurysms were detected on MR angiography in 3% of patients (4/115), but MR angiography in 5 patients (4.3%) was insignificant for infectious aneurysm, which were detected using CT angiography (n = 3) and digital subtraction angiography (n = 2) during follow-up. Among the 9 infectious aneurysm patients, aneurysm rupture occurred in 4 (44%), with a median period of aneurysm rupture of 5 days. The detection rate of brain MRI for neuroimaging abnormality in newly diagnosed left-sided infective endocarditis patients was high (77%), even without neurological symptoms (70%).