Dataset Information

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.

ABSTRACT: Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer's disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC > ?0.9) but substantially less accurate (AUC?=?0.76 for AD, AUC?=?0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC?=?0.73, compared to AUC?=?0.80 for the training set. A relatively low accuracy by clinicians (AUC?=?0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies.

SUBMITTER: Kloppel S

PROVIDER: S-EPMC4923764 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.

Klöppel Stefan S Peter Jessica J Ludl Anna A Pilatus Anne A Maier Sabrina S Mader Irina I Heimbach Bernhard B Frings Lars L Egger Karl K Dukart Juergen J Schroeter Matthias L ML Perneczky Robert R Häussermann Peter P Vach Werner W Urbach Horst H Teipel Stefan S Hüll Michael M Abdulkadir Ahmed A

Journal of Alzheimer's disease : JAD 20150101 4

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer's disease (AD), frontotemporal dementia ...[more]

PMID: 26401773

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Project description:BackgroundDiagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.ObjectiveThis study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.MethodsWe introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions.ResultsOn a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions.ConclusionsOur experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

Project description:BackgroundNeurofilament light in serum (sNfL) is a biomarker for axonal damage with elevated levels in many neurological disorders, including neurodegenerative dementias. Since within-group variation of sNfL is large and concentrations increase with aging, sNfL's clinical use in memory clinic practice remains to be established. The objective of the current study was to evaluate the clinical use of serum neurofilament light (sNfL), a cross-disease biomarker for axonal damage, in a tertiary memory clinic cohort.MethodsSix neurologists completed questionnaires regarding the usefulness of sNfL (n = 5-42 questionnaires/neurologist). Patients that visited the Alzheimer Center Amsterdam for the first time between May and October 2019 (n = 109) were prospectively included in this single-center implementation study. SNfL levels were analyzed on Simoa and reported together with normal values in relation to age, as part of routine diagnostic work-up and in addition to cerebrospinal fluid (CSF) biomarker analysis.ResultsSNfL was perceived as useful in 53% (n = 58) of the cases. SNfL was more often perceived as useful in patients < 62 years (29/48, 60%, p = 0.05) and males (41/65, 63%, p < 0.01). Availability of CSF biomarker results at time of result discussion had no influence. We observed non-significant trends for increased perceived usefulness of sNfL for patients with the diagnosis subjective cognitive decline (64%), psychiatric disorder (71%), or uncertain diagnosis (67%). SNfL was mostly helpful to neurologists in confirming or excluding neurodegeneration. Whether sNfL was regarded as useful strongly depended on which neurologist filled out the questionnaire (ranging from 0 to 73% of useful cases/neurologist).DiscussionRegardless of the availability of CSF biomarker results, sNfL was perceived as a useful tool in more than half of the evaluated cases in a tertiary memory clinic practice. Based on our results, we recommend the analysis of the biomarker sNfL to confirm or exclude neurodegeneration in patients below 62 years old and in males.

Project description:OBJECTIVE:To evaluate diagnostic criteria for idiopathic normal pressure hydrocephalus (INPH) among patients with memory impairment, and to estimate the incidence of INPH. DESIGN:Prospective observational cross-section and cohort study of diagnostic accuracy. SETTING:Memory Disorders Clinic following referral by the medical practitioners. PARTICIPANTS:408 consecutive patients enrolled 2010-2014. OUTCOME MEASURES:Reference diagnostic test was the clinical judgement of an experienced specialist based on the presence of cognitive impairment and/or balance and gait disorders in the presence of dilated ventricles. Mini-Mental State Examination (MMSE), Tinetti balance and gait tests were performed before and 12 months after ventriculoperitoneal shunt surgery. The association between reference diagnosis, clinical and brain CT scan measurements was estimated by multivariate Poisson regression. Triage index diagnostic test scores were calculated from the regression coefficients, with diagnostic thresholds selected using receiver operating characteristic analysis. RESULTS:The presence of balance and/or gait disorders, especially fear of falling, difficulty standing on toes/heals, urinary disturbances, ventriculomegaly with Evans ratio greater than Combined Diagnostic Threshold (0.377-{Maximum width of posterior horns*0.0054}), strongly predict the diagnosis of INPH; while hallucinations and/or delusions and forgetfulness reduce the likelihood of the diagnosis. This triage index test had high sensitivity (95.2%) and specificity (91.7%). 62 of 408 (15%) participants with cognitive impairment had INPH, an incidence of 11.9/100 000/year and 120/100 000/year over 75 years. 96% of participants following shunting, compared with 45% of the non-shunted, improved by over 25% of available measurable improvement in either MMSE or balance/gait scores (51% difference; 95%?CI 28% to 74%; p<0.001), and 56% vs 5% improved by over 50% of maximum in both (51% difference; 95%?CI 30% to 73%; p<0.001). CONCLUSION:The triage index test score is a simple tool that may be useful for physicians to identify INPH diagnoses and need for referral for shunt surgery, which may improve cognitive, balance and gait functioning.

Project description:BackgroundWhile assessment tools can increase the detection of cognitive impairment, there is currently insufficient evidence regarding clinical outcomes based on screening for cognitive impairment in older adults.MethodsThe study purpose was to investigate whether Timed Up and Go dual-task test (TUGdt) results, based on TUG combined with two different verbal tasks (name different animals, TUGdt-NA, and recite months in reverse order, TUGdt-MB), predicted dementia incidence over a period of five years among patients (N = 186, mean = 70.7 years; 45.7% female) diagnosed with Subjective Cognitive Impairment (SCI) and Mild Cognitive Impairment (MCI) following assessment at two memory clinics. Associations between TUG parameters and dementia incidence were examined in Cox regression models.ResultsDuring follow-up time (median (range) 3.7 (0.1-6.1) years) 98 participants converted to dementia. Novel findings indicated that the TUGdt parameter words/time, after adjustment for age, gender, and education, can be used for the prediction of conversion to dementia in participants with SCI or MCI over a period of five years. Among the TUG-related parameters investigated, words/time showed the best predictive capacity, while time scores of TUG and TUGdt as well as TUGdt cost did not produce significant predictive results. Results further showed that the step parameter step length during TUGdt predicts conversion to dementia before adjustment for age, gender, and education. Optimal TUGdt cutoffs for predicting dementia at 2- and 4-year follow-up based on words/time were calculated. The sensitivity of the TUGdt cutoffs was high at 2-year follow-up: TUGdt-NA words/time, 0.79; TUGdt-MB words/time, 0.71; reducing respectively to 0.64 and 0.65 at 4-year follow-up.ConclusionsTUGdt words/time parameters have potential as cost-efficient tools for conversion-to-dementia risk assessment, useful for research and clinical purposes. These parameters may be able to bridge the gap of insufficient evidence for such clinical outcomes.Trial registrationClinicalTrials.gov Identifier: NCT05893524: https://www.Clinicaltrialsgov/study/NCT05893524?id=NCT05893524&rank=1 .

Dataset Information

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.

Publications

Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study.

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