Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored. Purpose To investigate the ability of GPT-4V and Gemini Pro Vision in generating differential diagnoses at different temperatures compared with radiologists using Radiology Diagnosis Please cases. Materials and Methods This retrospective study included Diagnosis Please cases published from January 2008 to October 2023. Input images included original images and captures of the textual patient history and figure legends (without imaging findings) from PDF files of each case. The LLMs were tasked with providing three differential diagnoses, repeated five times at temperatures 0, 0.5, and 1. Eight subspecialty-trained radiologists solved cases. An experienced radiologist compared generated and final diagnoses, considering the result correct if the generated diagnoses included the final diagnosis after five repetitions. Accuracy was assessed across models, temperatures, and radiology subspecialties, with statistical significance set at P < .007 after Bonferroni correction for multiple comparisons across the LLMs at the three temperatures and with radiologists. Results A total of 190 cases were included in neuroradiology (n = 53), multisystem (n = 27), gastrointestinal (n = 25), genitourinary (n = 23), musculoskeletal (n = 17), chest (n = 16), cardiovascular (n = 12), pediatric (n = 12), and breast (n = 5) subspecialties. Overall accuracy improved with increasing temperature settings (0, 0.5, 1) for both GPT-4V (41% [78 of 190 cases], 45% [86 of 190 cases], 49% [93 of 190 cases], respectively) and Gemini Pro Vision (29% [55 of 190 cases], 36% [69 of 190 cases], 39% [74 of 190 cases], respectively), although there was no evidence of a statistically significant difference after Bonferroni adjustment (GPT-4V, P = .12; Gemini Pro Vision, P = .04). The overall accuracy of radiologists (61% [115 of 190 cases]) was higher than that of Gemini Pro Vision at temperature 1 (T1) (P < .001), while no statistically significant difference was observed between radiologists and GPT-4V at T1 after Bonferroni adjustment (P = .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.

Amphiregulin normalizes altered circuit connectivity for social dominance of the CRTC3 knockout mouse.

Social hierarchy has a profound impact on social behavior, reward processing, and mental health. Moreover, lower social rank can lead to chronic stress and often more serious problems such as bullying victims of abuse, suicide, or attack to society. However, its underlying mechanisms, particularly their association with glial factors, are largely unknown. In this study, we report that astrocyte-derived amphiregulin plays a critical role in the determination of hierarchical ranks. We found that astrocytes-secreted amphiregulin is directly regulated by cAMP response element-binding (CREB)-regulated transcription coactivator 3 (CRTC3) and CREB. Mice with systemic and astrocyte-specific CRTC3 deficiency exhibited a lower social rank with reduced functional connectivity between the prefrontal cortex, a major social hierarchy center, and the parietal cortex. However, this effect was reversed by astrocyte-specific induction of amphiregulin expression, and the epidermal growth factor domain was critical for this action of amphiregulin. These results provide evidence of the involvement of novel glial factors in the regulation of social dominance and may shed light on the clinical application of amphiregulin in the treatment of various psychiatric disorders.

Use of a commercial artificial intelligence-based mammography analysis software for improving breast ultrasound interpretations.

OBJECTIVES: To evaluate the use of a commercial artificial intelligence (AI)-based mammography analysis software for improving the interpretations of breast ultrasound (US)-detected lesions. METHODS: A retrospective analysis was performed on 1109 breasts that underwent both mammography and US-guided breast biopsy. The AI software processed mammograms and provided an AI score ranging from 0 to 100 for each breast, indicating the likelihood of malignancy. The performance of the AI score in differentiating mammograms with benign outcomes from those revealing cancers following US-guided breast biopsy was evaluated. In addition, prediction models for benign outcomes were constructed based on clinical and imaging characteristics with and without AI scores, using logistic regression analysis. RESULTS: The AI software had an area under the receiver operating characteristics curve (AUROC) of 0.79 (95% CI, 0.79-0.82) in differentiating between benign and cancer cases. The prediction models that did not include AI scores (non-AI model), only used AI scores (AI-only model), and included AI scores (integrated model) had AUROCs of 0.79 (95% CI, 0.75-0.83), 0.78 (95% CI, 0.74-0.82), and 0.85 (95% CI, 0.81-0.88) in the development cohort, and 0.75 (95% CI, 0.68-0.81), 0.82 (95% CI, 0.76-0.88), and 0.84 (95% CI, 0.79-0.90) in the validation cohort, respectively. The integrated model outperformed the non-AI model in the development and validation cohorts (p < 0.001 for both). CONCLUSION: The commercial AI-based mammography analysis software could be a valuable adjunct to clinical decision-making for managing US-detected breast lesions. CLINICAL RELEVANCE STATEMENT: The commercial AI-based mammography analysis software could potentially reduce unnecessary biopsies and improve patient outcomes. KEY POINTS: • Breast US has high rates of false-positive interpretations. • A commercial AI-based mammography analysis software could distinguish mammograms having benign outcomes from those revealing cancers after US-guided breast biopsy. • A commercial AI-based mammography analysis software may improve interpretations for breast US-detected lesions.

Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.

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.

Risk estimation for idiopathic normal-pressure hydrocephalus: development and validation of a brain morphometry-based nomogram.

OBJECTIVES: To develop and validate a nomogram based on MRI features for predicting iNPH. METHODS: Patients aged ≥ 60 years (clinically diagnosed with iNPH, Parkinson's disease, or Alzheimer's disease or healthy controls) who underwent MRI including three-dimensional T1-weighted volumetric MRI were retrospectively identified from two tertiary referral hospitals (one hospital for derivation set and the other for validation set). Clinical and imaging features for iNPH were assessed. Deep learning-based brain segmentation software was used for 3D volumetry. A prediction model was developed using logistic regression and transformed into a nomogram. The performance of the nomogram was assessed with respect to discrimination and calibration abilities. The nomogram was internally and externally validated. RESULTS: A total of 452 patients (mean age ± SD, 73.2 ± 6.5 years; 200 men) were evaluated as the derivation set. One hundred eleven and 341 patients were categorized into the iNPH and non-iNPH groups, respectively. In multivariable analysis, high-convexity tightness (odds ratio [OR], 35.1; 95% CI: 4.5, 275.5), callosal angle < 90° (OR, 12.5; 95% CI: 3.1, 50.0), and normalized lateral ventricle volume (OR, 4.2; 95% CI: 2.7, 6.7) were associated with iNPH. The nomogram combining these three variables showed an area under the curve of 0.995 (95% CI: 0.991, 0.999) in the study sample, 0.994 (95% CI: 0.990, 0.998) in the internal validation sample, and 0.969 (95% CI: 0.940, 0.997) in the external validation sample. CONCLUSION: A brain morphometry-based nomogram including high-convexity tightness, callosal angle < 90°, and normalized lateral ventricle volume can help accurately estimate the probability of iNPH. KEY POINTS: • The nomogram with MRI findings (high-convexity tightness, callosal angle, and normalized lateral ventricle volume) helped in predicting the probability of idiopathic normal-pressure hydrocephalus. • The nomogram may facilitate the prediction of idiopathic normal-pressure hydrocephalus and consequently avoid unnecessary invasive procedures such as the cerebrospinal fluid tap test, drainage test, and cerebrospinal fluid shunt surgery.

Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: a systematic review and meta-analysis.

OBJECTIVES: To investigate the diagnostic performance of T2*-weighted gradient echo (GRE) imaging, susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) in differentiating multiple system atrophy-parkinsonian type (MSA-P) from Parkinson's disease (PD). METHODS: A systematic literature search through the MEDLINE and EMBASE databases was performed, starting on September 8, 2020, to identify studies evaluating the diagnostic performance of putaminal hypointensity on T2* GRE or SWI and phase shift on QSM in differentiating MSA-P from PD. The pooled sensitivity and specificity were obtained using hierarchical logistic regression modeling and hierarchical summary receiver operating characteristic (HSROC) modeling. The pooled diagnostic yields of T2* GRE, SWI, or QSM among MSA-P patients were calculated using the DerSimonian-Laird random-effects model. RESULTS: Twelve original articles with 985 patients were finally included. SWI was performed in seven studies, T2* GRE was performed in three studies, and QSM was performed in two studies. The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. The area under the HSROC curve was 0.87 (95% CI 0.84-0.90). The Higgins I2 statistic calculations revealed considerable heterogeneity in terms of both sensitivity (I2 = 72.12%) and specificity (I2 = 70.38%). The coupled forest plot revealed the threshold effect. For the nine studies in which area under the curve (AUC) was obtainable, the AUC ranged from 0.68 to 0.947, with a median of 0.819. The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%). CONCLUSIONS: Putaminal hypointensity on T2* GRE or SWI and phase shift on QSM might be a promising diagnostic tool in differentiating MSA-P from PD. Further large multicenter prospective study is warranted. KEY POINTS: • Three different index tests, definitions of positive image findings, thresholds, the way how to draw ROIs, reference standard, and MRI parameters could affect the heterogeneity of the study. • The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. • The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%).

Diagnostic yield of MR myelography in patients with newly diagnosed spontaneous intracranial hypotension: a systematic review and meta-analysis.

OBJECTIVES: To investigate the pooled diagnostic yield of MR myelography in patients with newly diagnosed spontaneous intracranial hypotension (SIH). METHODS: A literature search of the MEDLINE/PubMed and Embase databases was conducted until July 25, 2021, including studies with the following inclusion criteria: (a) population: patients with newly diagnosed SIH; (b) diagnostic modality: MR myelography or MR myelography with intrathecal gadolinium for evaluation of CSF leakage; (c) outcomes: diagnostic yield of MR myelography or MR myelography with intrathecal gadolinium. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. DerSimonian-Laird random-effects modeling was used to calculate the pooled estimates. Subgroup analysis regarding epidural fluid collection and meta-regression were additionally performed. RESULTS: Fifteen studies with 643 patients were included. Eight studies used MR myelography with intrathecal gadolinium, and 11 used MR myelography. The overall quality of the included studies was moderate. The pooled diagnostic yield of MR myelography was 86% (95% CI, 80-91%) and that of MR myelography with intrathecal gadolinium was 83% (95% CI, 51-96%). There was no significant difference in pooled diagnostic yield between MR myelography and MR myelography with intrathecal gadolinium (p = 0.512). In subgroup analysis, the pooled diagnostic yield of the epidural fluid collection was 91% (95% CI, 84-94%). In meta-regression, the diagnostic yield was unaffected regardless of consecutive enrollment, magnet strength, or 2D/3D. CONCLUSIONS: MR myelography had a high diagnostic yield in patients with SIH. MR myelography is non-invasive and not inferior to MR myelography with intrathecal gadolinium. KEY POINTS: • The pooled diagnostic yield of MR myelography was 86% (95% CI, 80-91%) in patients with spontaneous intracranial hypotension. • There was no significant difference in pooled diagnostic yield between MR myelography and MR myelography with intrathecal gadolinium. • MR myelography is non-invasive and not inferior to MR myelography with intrathecal gadolinium.

Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance of hippocampal volumetry for Alzheimer's disease (AD) or mild cognitive impairment (MCI). METHODS: The MEDLINE and Embase databases were searched for articles that evaluated the diagnostic performance of hippocampal volumetry in differentiating AD or MCI from normal controls, published up to March 6, 2022. The quality of the articles was evaluated by the QUADAS-2 tool. A bivariate random-effects model was used to pool sensitivity, specificity, and area under the curve. Sensitivity analysis and meta-regression were conducted to explain study heterogeneity. The diagnostic performance of entorhinal cortex volumetry was also pooled. RESULTS: Thirty-three articles (5157 patients) were included. The pooled sensitivity and specificity for AD were 82% (95% confidence interval [CI], 77-86%) and 87% (95% CI, 82-91%), whereas those for MCI were 60% (95% CI, 51-69%) and 75% (95% CI, 67-81%), respectively. No difference in the diagnostic performance was observed between automatic and manual segmentation (p = 0.11). MMSE scores, study design, and the reference standard being used were associated with study heterogeneity (p < 0.01). Subgroup analysis demonstrated a higher diagnostic performance of entorhinal cortex volumetry for both AD (pooled sensitivity: 88% vs. 79%, specificity: 92% vs. 89%, p = 0.07) and MCI (pooled sensitivity: 71% vs. 55%, specificity: 83% vs. 68%, p = 0.06). CONCLUSIONS: Our meta-analysis demonstrated good diagnostic performance of hippocampal volumetry for AD or MCI. Entorhinal cortex volumetry might have superior diagnostic performance to hippocampal volumetry. However, due to a small number of studies, the diagnostic performance of entorhinal cortex volumetry is yet to be determined. KEY POINTS: • The pooled sensitivity and specificity of hippocampal volumetry for Alzheimer's disease were 82% and 87%, whereas those for mild cognitive impairment were 60% and 75%, respectively. • No significant difference in the diagnostic performance was observed between automatic and manual segmentation. • Subgroup analysis demonstrated superior diagnostic performance of entorhinal cortex volumetry for AD (pooled sensitivity: 88%, specificity: 92%) and MCI (pooled sensitivity: 71%, specificity: 83%).

Prognostic value of diffusion-weighted imaging in patients with newly diagnosed sporadic Creutzfeldt-Jakob disease.

OBJECTIVES: To evaluate clinico-radiologic markers that predict poor overall survival (OS) in sporadic Creutzfeldt-Jakob disease (sCJD) and to develop a prognostic model. MATERIALS AND METHODS: Patients with newly diagnosed sCJD were included who underwent diffusion-weighted imaging (DWI) from February 2000 to July 2020. The impact of 9 clinico-radiologic features on OS was analyzed using univariable and multivariable Cox proportional hazards regression model. The DWI prognostic score model was generated. The weighted kappa was calculated for interobserver agreement. RESULTS: Sixty patients (mean age ± SD, 61.0 ± 9.7 years, 32 women) were included. Univariable analysis showed positive associations between poor OS and patient age (p = 0.003), extent of involved cortical lobes (p = 0.11), involvement of caudate nucleus (p = 0.07), and putamen (p = 0.04). Multivariable analysis demonstrated two independent prognostic factors: age ≥ 60 (HR 2.65, 95% CI, 1.41-4.98), and diffusion restriction in caudate nucleus and putamen (HR 2.24, 95% CI, 1.15-4.37). Based on these features, the DWI prognostic score model was generated: low-risk (0-1 point), intermediate-risk (2-3 points), and high-risk (4-5 points) groups. Median OS in high-risk group was 1.7 months, which was significantly shorter than those in the intermediate-risk (14.2 months) and low-risk (26.5 months) groups (p < 0.001). Interobserver agreements were excellent (κ = 0.91-0.92). CONCLUSIONS: Our study demonstrated that age and diffusion restriction in caudate nucleus and putamen were the independent prognostic factors of poor overall survival in sporadic Creutzfeldt-Jakob disease. Our DWI prognostic score model may be useful in clinical settings for disease stratification. KEY POINTS: • Age ≥ 60, and diffusion restriction in caudate nucleus and putamen were the independent prognostic factors of poor overall survival in sCJD. • Based on our DWI prognostic score model, median overall survival in high-risk group was 1.7 months, which was significantly shorter than those in the intermediate-risk group (14.2 months) and low-risk group (26.5 months) (p < 0.001). • The proposed DWI prognostic score model may be useful in clinical settings for disease stratification.

Pure Prosodic Type of Primary Progressive Apraxia of Speech Mimicking Nonfluent Aphasia and Later Progressing to Corticobasal Syndrome.

Primary progressive apraxia of speech (PPAOS), a rare neurodedegenerative disorder, can be subdivided into predominant phonetic or prosodic type. Pure prosodic type of PPAOS as an isolated disorder has been hardly found. We present 2 cases of patients with pure prosodic PPAOS who initially were misdiagnosed as nonfluent variant of primary progressive aphasia and later turned out to be corticobasal syndrome. A 65-year-old woman and a 72-year-old man were referred to our speech-language clinic under the clinical impression of nonfluent variant of primary progressive aphasia. The neurological examinations revealed no definite abnormalities except for slow and effortful speech with the production of simple sentences. However, their receptive and expressive language abilities were normal. Their brain magnetic resonance imaging was unremarkable. We initially entertained the diagnosis of pure prosodic type of PPAOS. During several years of follow up, they gradually developed extrapyramidal symptoms which are compatible with corticobasal syndrome. The characteristics of the patients and the results of neuroimaging studies are discussed.

Impact of Brain MRI on the Diagnosis of Infective Endocarditis and Treatment Decisions: Systematic Review and Meta-Analysis.

BACKGROUND. Despite increasing use of brain MRI to evaluate patients with suspected infective endocarditis, the clinical impact of brain MRI in this setting has not yet been systematically reviewed. OBJECTIVE. The purpose of this study was to evaluate the frequency of brain MRI findings in patients with suspected or confirmed infective endocarditis and to determine the impact of such findings on clinical decisions. EVIDENCE ACQUISITION. A systematic search of the PubMed, Embase, and Cochrane databases was performed from January 1, 1990, to December 31, 2020, to identify original research investigations of brain MRI in patients with suspected or confirmed infective endocarditis. Study quality was assessed with QUADAS-2. Study endpoints included the frequency of brain MRI findings and the frequency of diagnostic modifications, modification of therapeutic plan, and modification of valve surgery plan resulting from MRI findings. Frequencies were pooled by means of the inverse variance method. Subgroup analysis was performed. EVIDENCE SYNTHESIS. A total of 21 studies with 2133 patients were included. Overall study quality was considered moderate. In terms of brain MRI findings, the pooled frequency of acute ischemic lesions was 61.9% (95% CI, 50.7-71.9%); of cerebral microbleeds, 52.9% (95% CI, 41.6-63.9%); hemorrhagic lesions, 24.7% (95% CI, 15.1-37.9%); abscess or meningitis, 9.5% (95% CI, 5.6-15.6%); and intracranial mycotic aneurysm, 6.2% (95% CI, 4.0-9.4%). Subgroup analysis after exclusion of three studies in which neurologic findings were the indication for all brain MRI examinations yielded similar frequencies of these findings. Six studies included results on the impact of brain MRI findings on clinical decisions. The frequencies of diagnostic modifications in two studies were 5.4% and 32.1%. The pooled frequency of therapeutic plan modification in six studies was 12.8% (95% CI, 6.5-23.7%) and of surgical plan modification in five studies was 14.2% (95% CI, 8.2-23.4%). CONCLUSION. In patients with suspected or confirmed infective endocarditis, brain MRI examinations commonly show relevant abnormalities that affect diagnostic and therapeutic clinical decisions. CLINICAL IMPACT. The findings support a potential role for screening brain MRI in the evaluation of patients with suspected or confirmed infective endocarditis, regardless of the presence or absence of neurologic symptoms.

Association between ARID2 and RAS-MAPK pathway in intellectual disability and short stature.

BACKGROUND: ARID2 belongs to the Switch/sucrose non-fermenting complex, in which the genetic defects have been found in patients with dysmorphism, short stature and intellectual disability (ID). As the phenotypes of patients with ARID2 mutations partially overlap with those of RASopathy, this study evaluated the biochemical association between ARID2 and RAS-MAPK pathway. METHODS: The phenotypes of 22 patients with either an ARID2 heterozygous mutation or haploinsufficiency were reviewed. Comprehensive molecular analyses were performed using somatic and induced pluripotent stem cells (iPSCs) of a patient with ARID2 haploinsufficiency as well as using the mouse model of Arid2 haploinsufficiency by CRISPR/Cas9 gene editing. RESULTS: The phenotypic characteristics of ARID2 deficiency include RASopathy, Coffin-Lowy syndrome or Coffin-Siris syndrome or undefined syndromic ID. Transient ARID2 knockout HeLa cells using an shRNA increased ERK1 and ERK2 phosphorylation. Impaired neuronal differentiation with enhanced RAS-MAPK activity was observed in patient-iPSCs. In addition, Arid2 haploinsufficient mice exhibited reduced body size and learning/memory deficit. ARID2 haploinsufficiency was associated with reduced IFITM1 expression, which interacts with caveolin-1 (CAV-1) and inhibits ERK activation. DISCUSSION: ARID2 haploinsufficiency is associated with enhanced RAS-MAPK activity, leading to reduced IFITM1 and CAV-1 expression, thereby increasing ERK activity. This altered interaction might lead to abnormal neuronal development and a short stature.

Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer's disease: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance and reliability of the medial temporal lobe atrophy (MTA) scale in patients with Alzheimer's disease. METHODS: A systematic literature search of MEDLINE and EMBASE databases was performed to select studies that evaluated the diagnostic performance or reliability of MTA scale, published up to January 21, 2021. Pooled estimates of sensitivity and specificity were calculated using a bivariate random-effects model. Pooled correlation coefficients for intra- and interobserver agreements were calculated using the random-effects model based on Fisher's Z transformation of correlations. Meta-regression was performed to explain the study heterogeneity. Subgroup analysis was performed to compare the diagnostic performance of the MTA scale and hippocampal volumetry. RESULTS: Twenty-one original articles were included. The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% (95% CI, 68-79%) and 88% (95% CI, 83-91%), respectively. The area under the curve of the MTA scale was 0.88 (95% CI, 0.84-0.90). Meta-regression demonstrated that the difference in the method of rating the MTA scale was significantly associated with study heterogeneity (p = 0.04). No significant difference was observed in five studies regarding the diagnostic performance between MTA scale and hippocampal volumetry (p = 0.40). The pooled correlation coefficients for intra- and interobserver agreements were 0.85 (95% CI, 0.69-0.93) and 0.83 (95% CI, 0.66-0.92), respectively. CONCLUSIONS: Our meta-analysis demonstrated a good diagnostic performance and reliability of the MTA scale in Alzheimer's disease. KEY POINTS: • The pooled sensitivity and specificity of the MTA scale in differentiating Alzheimer's disease from healthy control were 74% and 88%, respectively. • There was no significant difference in the diagnostic performance between MTA scale and hippocampal volumetry. • The reliability of MTA scale was excellent based on the pooled correlation coefficient for intra- and interobserver agreements.

Diagnostic performance of loss of nigral hyperintensity on susceptibility-weighted imaging in parkinsonism: an updated meta-analysis.

OBJECTIVES: To evaluate diagnostic performance of loss of nigral hyperintensity on SWI in differentiating idiopathic Parkinson's disease (IPD) or primary parkinsonism (including IPD and Parkinson-plus syndrome) from healthy/disease controls. METHODS: MEDLINE/PubMed and EMBASE databases were searched to identify original articles investigating the diagnostic performance of loss of nigral hyperintensity for differentiating IPD or primary parkinsonism from healthy/disease control, up to April 3, 2020. Pooled sensitivity and specificity were calculated using a bivariate random-effects model. The proportion of nondiagnostic scan, inter- and intrareader agreement, and the proportion of concordance between clinical laterality and imaging asymmetry were also pooled. RESULTS: Nineteen articles covering 2125 patients (1097 with primary parkinsonism, 1028 healthy/disease controls) were included. For discrimination between IPD and healthy/disease controls, pooled sensitivity and specificity were 0.96 (95% CI, 0.91-0.98) and 0.95 (95% CI, 0.92-0.97). For discrimination between primary parkinsonism and healthy/disease controls, pooled sensitivity and specificity were 0.87 (95% CI, 0.75-0.94) and 0.93 (95% CI, 0.85-0.97). The pooled proportion of non-diagnostic scans on random-effects modeling was 4.2% (95% CI, 2.5-6.9%). The inter- and intrareader agreements were almost perfect, with the pooled coefficients being 0.84 (95% CI, 0.78-0.89) and 0.96 (95% CI, 0.89-0.99), respectively. The pooled proportion of concordant cases was 69.3% (95% CI, 58.4-78.4%). CONCLUSIONS: Loss of nigral hyperintensity on SWI can differentiate IPD or primary parkinsonism from a healthy/disease control group with high accuracy. However, the proportion of non-diagnostic scans is not negligible and must be taken into account. KEY POINTS: • For discrimination between idiopathic Parkinson's disease and healthy/disease controls, pooled sensitivity and specificity of loss of nigral hyperintensity were 0.96 and 0.95. • For discrimination between primary parkinsonism and healthy/disease controls, pooled sensitivity and specificity of loss of nigral hyperintensity were 0.87 and 0.93. • The pooled proportion of non-diagnostic scans on random-effects modeling was 4.2%.

Diagnostic performance and interobserver agreement of the callosal angle and Evans' index in idiopathic normal pressure hydrocephalus: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance and interobserver agreement of the callosal angle and Evans' index in idiopathic normal pressure hydrocephalus (iNPH). METHODS: A systematic search of MEDLINE and EMBASE was performed to find studies assessing the diagnostic performance or interobserver agreement of the callosal angle and Evans' index in iNPH. Pooled sensitivity and specificity of the two radiologic indices were calculated. The area under the curve (AUC) was obtained based on a hierarchical summary receiver operating characteristic curve. The diagnostic performances of both radiologic indices were compared in subgroup analysis. To evaluate interobserver agreement, the pooled correlation coefficient was calculated. RESULTS: Ten original articles (874 patients) were included. The pooled sensitivity and specificity of the callosal angle in the diagnosis of iNPH were 91% (95% CI, 86-94%) and 93% (95% CI, 89-96%), respectively. The pooled sensitivity and specificity of Evans' index were 96% (95% CI, 47-100%) and 83% (95% CI, 77-88%), respectively. Subgroup analysis demonstrated a significant higher specificity of the callosal angle than that of Evans' index (p < 0.01). The AUC of the callosal angle and Evans' index were 0.97 (95% CI, 0.95-0.98) and 0.87 (95% CI, 0.84-0.90), respectively. The pooled correlation coefficients for the callosal angle and Evans' index were 0.92 (95% CI, 0.82-0.96) and 0.92 (95% CI, 0.83-0.97), respectively. CONCLUSIONS: Our meta-analysis demonstrated a high performance of the callosal angle in the diagnosis of iNPH. Evans' index showed reasonable diagnostic performance with high sensitivity but low specificity. Interobserver agreements were excellent in both radiologic indices. KEY POINTS: • Callosal angle showed high diagnostic performance in idiopathic normal pressure hydrocephalus. • Evans' index showed reasonable diagnostic performance with high sensitivity but low specificity. • Interobserver agreements were excellent in both callosal angle and Evans' index.

Diagnostic value of diffusion-weighted brain magnetic resonance imaging in patients with sporadic Creutzfeldt-Jakob disease: a systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic yield and performance of DWI in patients with sporadic CJD (sCJD). METHODS: A systematic literature search of the MEDLINE and EMBASE databases was performed, since their inception up to July 28, 2020. Pooled diagnostic yield of diffusion-weighted imaging was calculated using DerSimonian-Laird random-effects model. Pooled diagnostic performance of DWI (sensitivity, specificity, and area under the curve) in diagnosing sCJD among patients with rapidly progressive dementia was calculated using a bivariate random-effects model. Subgroup analysis and meta-regression were performed. RESULTS: Fifteen original articles with a total of 1144 patients with sCJD were included. The pooled diagnostic yield was 91% (95% confidence interval [CI], 86 to 94%); summary sensitivity, 91% (95% CI, 84 to 95%); and specificity, 97% (95% CI, 94 to 99%). The area under the hierarchical summary receiver operating characteristic curve was 0.99 (95% CI, 0.97-0.99). Simultaneous involvement of the neocortex and striatum was the most common finding, and the neocortex was the most common site to be involved on DWI followed by striatum, thalamus, and cerebellum. Subgroup analysis and meta-regression demonstrated significant heterogeneity among the studies associated with the reference standards used for diagnosis of sCJD. CONCLUSIONS: DWI showed excellent diagnostic value in diagnosis of sporadic Creutzfeldt-Jakob disease among patients with rapidly progressive dementia. Simultaneous involvement of the neocortex and striatum was the most common finding, and the neocortex was the most common site to be involved on diffusion-weighted imaging followed by striatum, thalamus, and cerebellum. KEY POINTS: • The pooled diagnostic yield of diffusion-weighted imaging in sporadic Creutzfeldt-Jakob disease was 91%. • The diagnostic performance of diffusion-weighted imaging for predicting sporadic Creutzfeldt-Jakob disease among patients with rapidly progressive dementia was excellent, with pooled sensitivity, 91%, and specificity, 97%. • Simultaneous involvement in the neocortex and striatum was most commonly seen on diffusion-weighted imaging (60%), followed by the neocortex without striatum (30%), thalamus (21%), cerebellum (8%), and striatum without neocortex (7%).

The cerebellum could serve as a potential imaging biomarker of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment.

BACKGROUND AND PURPOSE: As part of network-specific neurodegeneration, changes in cerebellar gray matter (GM) volume and impaired cerebello-cerebral functional networks have been reported in Alzheimer disease (AD). Compared with healthy controls, a volume loss in the cerebellum has been observed in patients with continuum of AD. However, little is known about the anatomical or functional changes in patients with clinical AD but no brain amyloidosis. We aimed to identify the relationship between cerebellar volume and dementia conversion of amyloid-negative mild cognitive impairment (MCI). METHODS: This study was a retrospective cohort study of patients over the age 50 years with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center with no less than a 36-month follow-up period. All subjects underwent detailed neuropsychological tests, 3 T brain magnetic resonance imaging scans including three-dimensional T1 imaging, and fluorine-18[F18 ]-florbetaben amyloid positron emission tomography scans. A spatially unbiased atlas template of the cerebellum and brainstem was used for analyzing cerebellar GM volume. RESULTS: During the 36 months of follow-up, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairments in all visual memory tasks. In terms of volumetric analysis, reduced crus I/II volume adjusted with total intracranial volume, and age was observed in the converter group. CONCLUSIONS: Significant cerebellar GM atrophy involving the bilateral crus I/II may be a novel imaging biomarker for predicting dementia progression in amyloid-negative amnestic MCI patients.

Early Impairment in the Ventral Visual Pathway Can Predict Conversion to Dementia in Patients With Amyloid-negative Amnestic Mild Cognitive Impairment.

BACKGROUND: Around 15% to 20% of patients with clinically probable Alzheimer disease have been found to have no significant Alzheimer pathology on amyloid positron emission tomography. A previous study showed that conversion to dementia from amyloid-negative mild cognitive impairment (MCI) was observed in up to 11% of patients, drawing attention to this condition. OBJECT: We gathered the detailed neuropsychological and neuroimaging data of this population to elucidate factors for conversion to dementia from amyloid-negative amnestic MCI. METHODS: This study was a single-institutional, retrospective cohort study of amyloid-negative MCI patients over age 50 with at least 36 months of follow-up. All subjects underwent detailed neuropsychological testing, 3 tesla brain magnetic resonance imaging), and fluorine-18(18F)-florbetaben amyloid positron emission tomography scans. RESULTS: During the follow-up period, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairment in all visual memory tasks. The volumetric analysis revealed that the converter group had significantly reduced total hippocampal volume on the right side, gray matter volume in the right lateral temporal, lingual gyri, and occipital pole. CONCLUSION: Our study showed that reduced gray matter volume related to visual memory processing may predict clinical progression in this amyloid-negative MCI population.

Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network.

BACKGROUND: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). METHODS: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. RESULTS: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. CONCLUSIONS: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.

De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

BACKGROUND: High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data. OBJECTIVE: This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images. METHODS: A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database. RESULTS: The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named "Deface program" for medical images as an open-source project. CONCLUSIONS: We developed deep learning-based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.