1
|
Rhodius-Meester HFM, Paajanen T, Lötjönen J. cCOG Web-Based Cognitive Assessment Tool. Methods Mol Biol 2024; 2785:311-320. [PMID: 38427202 DOI: 10.1007/978-1-0716-3774-6_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Cognitive testing is an essential part of clinical diagnostics and clinical trials in Alzheimer's disease. Digital cognitive tests hold a great opportunity to provide more versatile and cost-efficient patient pathways through flexible testing including at home. In this work, we describe a web-based cognitive test, cCOG, that can be used in screening, differential diagnosis, and monitoring the progression of neurodegenerative diseases.
Collapse
Affiliation(s)
- Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Department of Internal medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.
- Department of Geriatric Medicine, The Memory Clinic, Oslo University Hospital, Oslo, Norway.
| | - Teemu Paajanen
- Work ability and Working Careers, Finnish Institute of Occupational Health, Helsinki, Finland
| | | |
Collapse
|
2
|
Dar SA, Imtiaz N. Classification of neuroimaging data in Alzheimer's disease using particle swarm optimization: A systematic review. Appl Neuropsychol Adult 2023:1-12. [PMID: 36719791 DOI: 10.1080/23279095.2023.2169886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
AIM Particle swarm optimization (PSO) is an algorithm that involves the optimization of Non-linear and Multidimensional problems to reach the best solutions with minimal parameterization. This metaheuristic model has frequently been used in the Pathological domain. This optimization model has been used in diverse forms while predicting Alzheimer's disease. It is a robust algorithm that works on linear and multi-modal data while predicting Alzheimer's disease. PSO techniques have been in action for quite some time for detecting various diseases and this paper systematically reviews the papers on various kinds of PSO techniques. METHODS To perform the systematic review, PRISMA guidelines were followed and a Boolean search ("particle swarm optimization" OR "PSO") AND Neuroimaging AND (Alzheimer's disease prediction OR classification OR diagnosis) were performed. The query was run in 4-reputed databases: Google Scholar, Scopus, Science Direct, and Wiley publications. RESULTS For the final analysis, 10 papers were incorporated for qualitative and quantitative synthesis. PSO has shown a dominant character while handling the uni-modal as well as the multi-modal data while predicting the conversion from MCI to Alzheimer's. It can be seen from the table that almost all the 10 reviewed papers had MRI-driven data. The accuracy rate was accentuated while adding other modalities or Neurocognitive measures. CONCLUSIONS Through this algorithm, we are providing an opportunity to other researchers to compare this algorithm with other state-of-the-art algorithms, while seeing the classification accuracy, with the aim of early prediction and progression of MCI into Alzheimer's disease.
Collapse
Affiliation(s)
- Suhail Ahmad Dar
- Department of Psychology, Aligarh Muslim University, Aligarh, India
| | - Nasheed Imtiaz
- Department of Psychology, Aligarh Muslim University, Aligarh, India
| |
Collapse
|
3
|
Xu Q, Xie W, Liao B, Hu C, Qin L, Yang Z, Xiong H, Lyu Y, Zhou Y, Luo A. Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review. J Healthc Eng 2023; 2023:9919269. [PMID: 36776958 DOI: 10.1155/2023/9919269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/05/2022] [Accepted: 11/24/2022] [Indexed: 02/05/2023]
Abstract
Background Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.
Collapse
|
4
|
van Gils AM, van de Beek M, van Unnik AAJM, Tolonen A, Handgraaf D, van Leeuwenstijn M, Lötjönen J, van der Flier WM, Lemstra A, Rhodius‐Meester HFM. Optimizing cCOG, a Web-based tool, to detect dementia with Lewy Bodies. Alzheimers Dement (Amst) 2022; 14:e12379. [PMID: 36569383 PMCID: PMC9773307 DOI: 10.1002/dad2.12379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/10/2022] [Accepted: 10/19/2022] [Indexed: 12/24/2022]
Abstract
Introduction Distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) is challenging due to overlapping presentations. We adapted a Web-based test tool, cCOG, by adding a visuospatial task and a brief clinical survey and assessed its ability to differentiate between DLB and AD. Methods We included 110 patients (n = 30 DLB, n = 32 AD dementia, and n = 48 controls with subjective cognitive decline (SCD)). Full cCOG comprises six cognitive subtasks and a survey addressing self-reported DLB core and autonomic features. First, we compared cCOG cognitive tasks to traditional neuropsychological tasks for all diagnostic groups and clinical questions to validated assessments of clinical features in DLB only. Then, we studied the performance of cCOG cognitive tasks and clinical questions, separately and combined, in differentiating diagnostic groups. Results cCOG cognitive tasks and clinical survey had moderate to strong correlations to standard neuropsychological testing (.61≤ r s ≤ .77) and to validated assessments of clinical features (.41≤ r s ≤ .65), except for fluctuations and REM-sleep behavior disorder (RBD) (r s = .32 and r s = .10). Full cCOG, including both cognitive tasks and brief survey had a diagnostic accuracy (acc) of 0.82 [95% CI 0.73-0.89], with good discrimination of DLB versus AD (acc 0.87 [0.76-0.95]) and DLB versus controls (acc 0.94 [0.86-0.98]). Conclusion We illustrated that cCOG aids in distinguishing DLB and AD patients by using remote assessment of cognition and clinical features. Our findings pave the way to a funneled, harmonized diagnostic process among memory clinics and, eventually, a more timely and accurate diagnosis of DLB and AD.
Collapse
Affiliation(s)
- Aniek M. van Gils
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | - Marleen van de Beek
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | - Annemartijn A. J. M. van Unnik
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | | | - Dédé Handgraaf
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | - Mardou van Leeuwenstijn
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | | | - Wiesje M. van der Flier
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands,Department of Epidemiology and Data SciencesVrije Universiteit AmsterdamAmsterdam UMCAmsterdamThe Netherlands
| | - Afina Lemstra
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands
| | - Hanneke F. M. Rhodius‐Meester
- Alzheimer Center AmsterdamNeurologyVrije Universiteit AmsterdamAmsterdam UMC location VUmcAmsterdamThe Netherlands,Amsterdam NeuroscienceNeurodegenerationAmsterdamThe Netherlands,Department of Internal MedicineGeriatric Medicine SectionVrije Universiteit AmsterdamAmsterdam UMCAmsterdamThe Netherlands
| |
Collapse
|
5
|
Huse S, Acharya S, Shukla S, J H, Sachdev A. Computer-Aided Detection and Diagnosis of Neurological Disorder. Cureus 2022; 14:e28032. [PMID: 36120284 PMCID: PMC9473453 DOI: 10.7759/cureus.28032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 08/15/2022] [Indexed: 12/01/2022] Open
Abstract
Nowadays, neurological problems are more regular, representing a worry to pregnant ladies, guardians, healthy babies, and kids. Neurological problems emerge in a wide assortment of structures, each with its arrangement of beginnings, inconveniences, and results. The conclusion of neurological illnesses is an evolving concern and predominantly troublesome difficulty for current medication. Current diagnosis advancements (e.g., MRI and EEG) produce immense information (in proportion and aspect) as location, checking, and therapy of nervous system illnesses. As a common rule, investigation of that enormous clinical information is performed physically by specialists to distinguish and figure out the irregularities. It is a genuinely troublesome errand for an individual to collect, make due, investigate, and absorb enormous amounts of information through visible review. As an outcome, the specialist has been requesting electronic conclusion frameworks known as “computer-aided diagnosis” that can consequently identify the nervous system irregularities utilizing the essential clinical information. This framework further develops uniformity of findings, builds treatment outcomes, protects lives, and lessens price and time. As of late, few examinations have improved the computer-aided design frameworks for the executives of enormous clinical information for determination appraisal. This paper investigates the difficulties of tremendous clinical information giving. This article fundamentally evaluated and looked at the exhibition of existing AI and comprehensive learning approaches for identifying nervous system illnesses. A far-reaching piece of this concentration also shows different modalities and illness-determined datasets that identify and record pictures, signs, addresses, and so forth. Restricted related works are additionally summed up on nervous system illnesses, as this space has essentially less work zeroed in on illness and recognition rules. A portion of the standard assessment measurements is likewise introduced in this review for improved outcome examination.
Collapse
|
6
|
Wannan CMJ, Pantelis C, Merritt AH, Tonge B, Syeda WT. Affinity scores: An individual-centric fingerprinting framework for neuropsychiatric disorders. Transl Psychiatry 2022; 12:322. [PMID: 35945206 DOI: 10.1038/s41398-022-02084-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/09/2022] Open
Abstract
Population-centric frameworks of biomarker identification for psychiatric disorders focus primarily on comparing averages between groups and assume that diagnostic groups are (1) mutually-exclusive, and (2) homogeneous. There is a paucity of individual-centric approaches capable of identifying individual-specific 'fingerprints' across multiple domains. To address this, we propose a novel framework, combining a range of biopsychosocial markers, including brain structure, cognition, and clinical markers, into higher-level 'fingerprints', capable of capturing intra-illness heterogeneity and inter-illness overlap. A multivariate framework was implemented to identify individualised patterns of brain structure, cognition and clinical markers based on affinity to other participants in the database. First, individual-level affinity scores defined each participant's "neighbourhood" across each measure based on variable-specific hop sizes. Next, diagnostic verification and classification algorithms were implemented based on multivariate affinity score profiles. To perform affinity-based classification, data were divided into training and test samples, and 5-fold nested cross-validation was performed on the training data. Affinity-based classification was compared to weighted K-nearest neighbours (KNN) classification. The framework was applied to the Australian Schizophrenia Research Bank (ASRB) dataset, which included data from individuals with chronic and treatment resistant schizophrenia and healthy controls. Individualised affinity scores provided a 'fingerprint' of brain structure, cognition, and clinical markers, which described the affinity of an individual to the representative groups in the dataset. Diagnostic verification capability was moderate to high depending on the choice of multivariate affinity metric. Affinity score-based classification achieved a high degree of accuracy in the training, nested cross-validation and prediction steps, and outperformed KNN classification in the training and test datasets. Affinity scores demonstrate utility in two keys ways: (1) Early and accurate diagnosis of neuropsychiatric disorders, whereby an individual can be grouped within a diagnostic category/ies that best matches their fingerprint, and (2) identification of biopsychosocial factors that most strongly characterise individuals/disorders, and which may be most amenable to intervention.
Collapse
|
7
|
Tabarestani S, Eslami M, Cabrerizo M, Curiel RE, Barreto A, Rishe N, Vaillancourt D, DeKosky ST, Loewenstein DA, Duara R, Adjouadi M. A Tensorized Multitask Deep Learning Network for Progression Prediction of Alzheimer’s Disease. Front Aging Neurosci 2022; 14:810873. [PMID: 35601611 PMCID: PMC9120529 DOI: 10.3389/fnagi.2022.810873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
With the advances in machine learning for the diagnosis of Alzheimer’s disease (AD), most studies have focused on either identifying the subject’s status through classification algorithms or on predicting their cognitive scores through regression methods, neglecting the potential association between these two tasks. Motivated by the need to enhance the prospects for early diagnosis along with the ability to predict future disease states, this study proposes a deep neural network based on modality fusion, kernelization, and tensorization that perform multiclass classification and longitudinal regression simultaneously within a unified multitask framework. This relationship between multiclass classification and longitudinal regression is found to boost the efficacy of the final model in dealing with both tasks. Different multimodality scenarios are investigated, and complementary aspects of the multimodal features are exploited to simultaneously delineate the subject’s label and predict related cognitive scores at future timepoints using baseline data. The main intent in this multitask framework is to consolidate the highest accuracy possible in terms of precision, sensitivity, F1 score, and area under the curve (AUC) in the multiclass classification task while maintaining the highest similarity in the MMSE score as measured through the correlation coefficient and the RMSE for all time points under the prediction task, with both tasks, run simultaneously under the same set of hyperparameters. The overall accuracy for multiclass classification of the proposed KTMnet method is 66.85 ± 3.77. The prediction results show an average RMSE of 2.32 ± 0.52 and a correlation of 0.71 ± 5.98 for predicting MMSE throughout the time points. These results are compared to state-of-the-art techniques reported in the literature. A discovery from the multitasking of this consolidated machine learning framework is that a set of hyperparameters that optimize the prediction results may not necessarily be the same as those that would optimize the multiclass classification. In other words, there is a breakpoint beyond which enhancing further the results of one process could lead to the downgrading in accuracy for the other.
Collapse
Affiliation(s)
- Solale Tabarestani
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
- *Correspondence: Solale Tabarestani,
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab and Harvard Medical School, Schepens Eye Research Institute, Massachusetts Eye and Ear, Boston, MA, United States
| | - Mercedes Cabrerizo
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Rosie E. Curiel
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
| | - Armando Barreto
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - Naphtali Rishe
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
| | - David Vaillancourt
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, United States
| | - Steven T. DeKosky
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Department of Neurology, University of Florida, Gainesville, FL, United States
| | - David A. Loewenstein
- Center for Cognitive Neuroscience and Aging, Psychiatry and Behavioral Sciences, University of Miami School of Medicine, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Ranjan Duara
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami Beach, FL, United States
| | - Malek Adjouadi
- Center for Advanced Technology and Education, Florida International University, Miami, FL, United States
- Florida Alzheimer’s Disease Research Center, University of Florida, Gainesville, FL, United States
- Malek Adjouadi,
| |
Collapse
|
8
|
Ramírez F, Gutiérrez M. Dual-Task Gait as a Predictive Tool for Cognitive Impairment in Older Adults: A Systematic Review. Front Aging Neurosci 2021; 13:769462. [PMID: 35002676 PMCID: PMC8740025 DOI: 10.3389/fnagi.2021.769462] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/06/2021] [Indexed: 01/21/2023] Open
Abstract
The use of the dual-task model as a cognitive-motor interface has been extensively investigated in cross-sectional studies as a training task in cognitive impairment. However, few existing longitudinal studies prove the usefulness of this tool as a clinical marker of cognitive impairment in older people. What is the evidence in prospective studies about dual-task gait as a predictor of cognitive impairment in older adults? This study aims to review and discuss the current state of knowledge in prospective studies on the use of dual-task gait as a predictive tool for cognitive impairment in older adults. The methodology used was a systematic review, according to the PRISMA criteria for the search, summarize and report. A search in 3 databases (Pubmed, Web of Science, and Scopus) was carried out until April 2021. The search terms used were: "(gait OR walking) AND (cognitive decline) AND (dual-task) AND (follow-up OR longitudinal OR long-term OR prospective OR cohort OR predict)." We included prospective research articles with older people with cognitive evaluation at the beginning and the end of the follow-up and dual-task gait paradigm as initial evaluation associated with the presentation of cognitive impairment prediction using any dual-task gait parameters. After exclusion criteria, 12 studies were reviewed. The results indicate that eight studies consider dual-task gait parameters a useful cognitive-motor tool, finding that some of the evaluated parameters of dual-task gait significantly correlate with cognitive impairment over time. The most promising DT parameters associated with cognitive impairment prediction seem to be gait speed, speed cost, DT time, numbers of words during DT, among others. In sum, this study reviews the variety of dual-task gait parameters and their relevance as a simple tool for early cognitive impairment screening, opening a diagnostic window for the screening of cognitive impairment in older people.
Collapse
Affiliation(s)
- Felipe Ramírez
- Programa Magíster en Kinesiología Gerontológica, Facultad de Ciencias, Universidad Mayor, Santiago, Chile
| | - Myriam Gutiérrez
- Escuela de Kinesiología, Facultad de Ciencias de la Salud, Universidad de Las Américas, Santiago, Chile
- Centro de Estudio del Movimiento Humano, Escuela de Kinesiología, Facultad de Odontología y Salud, Universidad Diego Portales, Santiago, Chile
- Unidad de Cerebro Saludable, Hospital Clínico Universidad de Chile, Santiago, Chile
| |
Collapse
|
9
|
van Gils AM, Visser LN, Hendriksen HM, Georges J, Muller M, Bouwman FH, van der Flier WM, Rhodius-Meester HF. Assessing the Views of Professionals, Patients, and Care Partners Concerning the Use of Computer Tools in Memory Clinics: International Survey Study. JMIR Form Res 2021; 5:e31053. [PMID: 34870612 PMCID: PMC8686488 DOI: 10.2196/31053] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/07/2021] [Accepted: 09/18/2021] [Indexed: 12/20/2022] Open
Abstract
Background Computer tools based on artificial intelligence could aid clinicians in memory clinics in several ways, such as by supporting diagnostic decision-making, web-based cognitive testing, and the communication of diagnosis and prognosis. Objective This study aims to identify the preferences as well as the main barriers and facilitators related to using computer tools in memory clinics for all end users, that is, clinicians, patients, and care partners. Methods Between July and October 2020, we sent out invitations to a web-based survey to clinicians using the European Alzheimer’s Disease Centers network and the Dutch Memory Clinic network, and 109 clinicians participated (mean age 45 years, SD 10; 53/109, 48.6% female). A second survey was created for patients and care partners. They were invited via Alzheimer Europe, Alzheimer’s Society United Kingdom, Amsterdam Dementia Cohort, and Amsterdam Aging Cohort. A total of 50 patients with subjective cognitive decline, mild cognitive impairment, or dementia (mean age 73 years, SD 8; 17/34, 34% female) and 46 care partners (mean age 65 years, SD 12; 25/54, 54% female) participated in this survey. Results Most clinicians reported a willingness to use diagnostic (88/109, 80.7%) and prognostic (83/109, 76.1%) computer tools. User-friendliness (71/109, 65.1%); Likert scale mean 4.5, SD 0.7), and increasing diagnostic accuracy (76/109, 69.7%; mean 4.3, SD 0.7) were reported as the main factors stimulating the adoption of a tool. Tools should also save time and provide clear information on reliability and validity. Inadequate integration with electronic patient records (46/109, 42.2%; mean 3.8, SD 1.0) and fear of losing important clinical information (48/109, 44%; mean 3.7, SD 1.2) were most frequently indicated as barriers. Patients and care partners were equally positive about the use of computer tools by clinicians, both for diagnosis (69/96, 72%) and prognosis (73/96, 76%). In addition, most of them thought favorably regarding the possibility of using the tools themselves. Conclusions This study showed that computer tools in memory clinics are positively valued by most end users. For further development and implementation, it is essential to overcome the technical and practical barriers of a tool while paying utmost attention to its reliability and validity.
Collapse
Affiliation(s)
- Aniek M van Gils
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Leonie Nc Visser
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institutet, Stockholm, Sweden
| | - Heleen Ma Hendriksen
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Femke H Bouwman
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Wiesje M van der Flier
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Epidemiology and Data Science, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| | - Hanneke Fm Rhodius-Meester
- Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands.,Department of Internal Medicine, Geriatric Medicine Section, Amsterdam Cardiovascular Sciences Institute, Amsterdam UMC, Location VUmc, Amsterdam, Netherlands
| |
Collapse
|
10
|
Dyrba M, Hanzig M, Altenstein S, Bader S, Ballarini T, Brosseron F, Buerger K, Cantré D, Dechent P, Dobisch L, Düzel E, Ewers M, Fliessbach K, Glanz W, Haynes JD, Heneka MT, Janowitz D, Keles DB, Kilimann I, Laske C, Maier F, Metzger CD, Munk MH, Perneczky R, Peters O, Preis L, Priller J, Rauchmann B, Roy N, Scheffler K, Schneider A, Schott BH, Spottke A, Spruth EJ, Weber MA, Ertl-Wagner B, Wagner M, Wiltfang J, Jessen F, Teipel SJ. Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: evaluation in Alzheimer's disease. Alzheimers Res Ther 2021; 13:191. [PMID: 34814936 PMCID: PMC8611898 DOI: 10.1186/s13195-021-00924-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022]
Abstract
Background Although convolutional neural networks (CNNs) achieve high diagnostic accuracy for detecting Alzheimer’s disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in clinical routine. One important reason for this is a lack of model comprehensibility. Recently developed visualization methods for deriving CNN relevance maps may help to fill this gap as they allow the visualization of key input image features that drive the decision of the model. We investigated whether models with higher accuracy also rely more on discriminative brain regions predefined by prior knowledge. Methods We trained a CNN for the detection of AD in N = 663 T1-weighted MRI scans of patients with dementia and amnestic mild cognitive impairment (MCI) and verified the accuracy of the models via cross-validation and in three independent samples including in total N = 1655 cases. We evaluated the association of relevance scores and hippocampus volume to validate the clinical utility of this approach. To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps, thereby allowing intuitive model inspection. Results Across the three independent datasets, group separation showed high accuracy for AD dementia versus controls (AUC ≥ 0.91) and moderate accuracy for amnestic MCI versus controls (AUC ≈ 0.74). Relevance maps indicated that hippocampal atrophy was considered the most informative factor for AD detection, with additional contributions from atrophy in other cortical and subcortical regions. Relevance scores within the hippocampus were highly correlated with hippocampal volumes (Pearson’s r ≈ −0.86, p < 0.001). Conclusion The relevance maps highlighted atrophy in regions that we had hypothesized a priori. This strengthens the comprehensibility of the CNN models, which were trained in a purely data-driven manner based on the scans and diagnosis labels. The high hippocampus relevance scores as well as the high performance achieved in independent samples support the validity of the CNN models in the detection of AD-related MRI abnormalities. The presented data-driven and hypothesis-free CNN modeling approach might provide a useful tool to automatically derive discriminative features for complex diagnostic tasks where clear clinical criteria are still missing, for instance for the differential diagnosis between various types of dementia. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-021-00924-2.
Collapse
Affiliation(s)
- Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.
| | - Moritz Hanzig
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Sebastian Bader
- Institute of Visual and Analytic Computing, University of Rostock, Rostock, Germany
| | | | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Daniel Cantré
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University, Goettingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | | | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Deniz B Keles
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research, Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany.,Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany.,Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany.,Systems Neurophysiology, Department of Biology, Darmstadt University of Technology, Darmstadt, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Ludwig Maximilian University, Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany.,Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University Munich, Munich, Germany
| | - Boris Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, Rostock University Medical Center, Rostock, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilian University, Munich, Germany.,Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department for Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, Goettingen, Germany.,Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | | |
Collapse
|
11
|
Pemberton HG, Zaki LAM, Goodkin O, Das RK, Steketee RME, Barkhof F, Vernooij MW. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 2021; 63:1773-1789. [PMID: 34476511 PMCID: PMC8528755 DOI: 10.1007/s00234-021-02746-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 06/02/2021] [Indexed: 12/22/2022]
Abstract
Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.
Collapse
Affiliation(s)
- Hugh G Pemberton
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK.
- UCL Queen Square Institute of Neurology, University College London, London, UK.
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
| | - Lara A M Zaki
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Olivia Goodkin
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ravi K Das
- Clinical, Educational and Health Psychology, University College London, London, UK
| | - Rebecca M E Steketee
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Radiology & Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Meike W Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| |
Collapse
|
12
|
Muurling M, Rhodius-Meester HFM, Pärkkä J, van Gils M, Frederiksen KS, Bruun M, Hasselbalch SG, Soininen H, Herukka SK, Hallikainen M, Teunissen CE, Visser PJ, Scheltens P, van der Flier WM, Mattila J, Lötjönen J, de Boer C. Gait Disturbances are Associated with Increased Cognitive Impairment and Cerebrospinal Fluid Tau Levels in a Memory Clinic Cohort. J Alzheimers Dis 2021; 76:1061-1070. [PMID: 32597806 PMCID: PMC7505008 DOI: 10.3233/jad-200225] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Gait analysis with accelerometers is a relatively inexpensive and easy to use method to potentially support clinical diagnoses of Alzheimer’s disease and other dementias. It is not clear, however, which gait features are most informative and how these measures relate to Alzheimer’s disease pathology. Objective: In this study, we tested if calculated features of gait 1) differ between cognitively normal subjects (CN), mild cognitive impairment (MCI) patients, and dementia patients, 2) are correlated with cerebrospinal fluid (CSF) biomarkers related to Alzheimer’s disease, and 3) predict cognitive decline. Methods: Gait was measured using tri-axial accelerometers attached to the fifth lumbar vertebra (L5) in 58 CN, 58 MCI, and 26 dementia participants, while performing a walk and dual task. Ten gait features were calculated from the vertical L5 accelerations, following principal component analysis clustered in four domains, namely pace, rhythm, time variability, and length variability. Cognitive decline over time was measured using MMSE, and CSF biomarkers were available in a sub-group. Results: Linear mixed models showed that dementia patients had lower pace scores than MCI patients and CN subjects (p < 0.05). In addition, we found associations between the rhythm domain and CSF-tau, especially in the dual task. Gait was not associated with CSF Aβ42 levels and cognitive decline over time as measured with the MMSE. Conclusion: These findings suggest that gait — particularly measures related to pace and rhythm — are altered in dementia and have a direct link with measures of neurodegeneration.
Collapse
Affiliation(s)
- Marijn Muurling
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Juha Pärkkä
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Denmark
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland.,Department of Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Merja Hallikainen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | | | - Casper de Boer
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| |
Collapse
|
13
|
Tohka J, van Gils M. Evaluation of machine learning algorithms for health and wellness applications: A tutorial. Comput Biol Med 2021; 132:104324. [PMID: 33774270 DOI: 10.1016/j.compbiomed.2021.104324] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 02/19/2021] [Accepted: 03/07/2021] [Indexed: 11/22/2022]
Abstract
Research on decision support applications in healthcare, such as those related to diagnosis, prediction, treatment planning, etc., has seen strongly growing interest in recent years. This development is thanks to the increase in data availability as well as to advances in artificial intelligence and machine learning research and access to computational resources. Highly promising research examples are published daily. However, at the same time, there are some unrealistic, often overly optimistic, expectations and assumptions with regard to the development, validation and acceptance of such methods. The healthcare application field introduces requirements and potential pitfalls that are not immediately obvious from the 'general data science' viewpoint. Reliable, objective, and generalisable validation and performance assessment of developed data-analysis methods is one particular pain-point. This may lead to unmet schedules and disappointments regarding true performance in real-life with as result poor uptake (or non-uptake) at the end-user side. It is the aim of this tutorial to provide practical guidance on how to assess performance reliably and efficiently and avoid common traps especially when dealing with application for health and wellness settings. Instead of giving a list of do's and don't s, this tutorial tries to build a better understanding behind these issues and presents both the most relevant performance evaluation criteria as well as approaches how to compute them. Along the way, we will indicate common mistakes and provide references discussing various topics more in-depth.
Collapse
|
14
|
Pekkala T, Hall A, Ngandu T, van Gils M, Helisalmi S, Hänninen T, Kemppainen N, Liu Y, Lötjönen J, Paajanen T, Rinne JO, Soininen H, Kivipelto M, Solomon A. Detecting Amyloid Positivity in Elderly With Increased Risk of Cognitive Decline. Front Aging Neurosci 2020; 12:228. [PMID: 32848707 PMCID: PMC7406705 DOI: 10.3389/fnagi.2020.00228] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/29/2020] [Indexed: 12/28/2022] Open
Abstract
The importance of early interventions in Alzheimer's disease (AD) emphasizes the need to accurately and efficiently identify at-risk individuals. Although many dementia prediction models have been developed, there are fewer studies focusing on detection of brain pathology. We developed a model for identification of amyloid-PET positivity using data on demographics, vascular factors, cognition, APOE genotype, and structural MRI, including regional brain volumes, cortical thickness and a visual medial temporal lobe atrophy (MTA) rating. We also analyzed the relative importance of different factors when added to the overall model. The model used baseline data from the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) exploratory PET sub-study. Participants were at risk for dementia, but without dementia or cognitive impairment. Their mean age was 71 years. Participants underwent a brain 3T MRI and PiB-PET imaging. PiB images were visually determined as positive or negative. Cognition was measured using a modified version of the Neuropsychological Test Battery. Body mass index (BMI) and hypertension were used as cardiovascular risk factors in the model. Demographic factors included age, gender and years of education. The model was built using the Disease State Index (DSI) machine learning algorithm. Of the 48 participants, 20 (42%) were rated as Aβ positive. Compared with the Aβ negative group, the Aβ positive group had a higher proportion of APOE ε4 carriers (53 vs. 14%), lower executive functioning, lower brain volumes, and higher visual MTA rating. AUC [95% CI] for the complete model was 0.78 [0.65-0.91]. MRI was the most effective factor, especially brain volumes and visual MTA rating but not cortical thickness. APOE was nearly as effective as MRI in improving detection of amyloid positivity. The model with the best performance (AUC 0.82 [0.71-0.93]) was achieved by combining APOE and MRI. Our findings suggest that combining demographic data, vascular risk factors, cognitive performance, APOE genotype, and brain MRI measures can help identify Aβ positivity. Detecting amyloid positivity could reduce invasive and costly assessments during the screening process in clinical trials.
Collapse
Affiliation(s)
- Timo Pekkala
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Anette Hall
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tiia Ngandu
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Seppo Helisalmi
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tuomo Hänninen
- Neurocenter/Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Nina Kemppainen
- Turku PET Centre, University of Turku, Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
| | - Yawu Liu
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | | | - Teemu Paajanen
- Finnish Institute of Occupational Health, Helsinki, Finland
| | - Juha O Rinne
- Turku PET Centre, University of Turku, Turku, Finland.,Division of Clinical Neurosciences, Turku University Hospital, Turku, Finland
| | - Hilkka Soininen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Neurocenter/Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Miia Kivipelto
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden.,Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland.,Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, United Kingdom
| | - Alina Solomon
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.,Division of Clinical Geriatrics, Center for Alzheimer Research, NVS, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
15
|
Turner RS, Stubbs T, Davies DA, Albensi BC. Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias. Front Neurol 2020; 11:496. [PMID: 32582013 PMCID: PMC7290039 DOI: 10.3389/fneur.2020.00496] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/06/2020] [Indexed: 12/21/2022] Open
Abstract
Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.
Collapse
Affiliation(s)
- R Scott Turner
- Department of Neurology, Georgetown University, Washington, DC, United States
| | - Terry Stubbs
- ActivMed, Practices & Research, Methuen, MA, United States
| | - Don A Davies
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
| |
Collapse
|
16
|
Gjerum L, Frederiksen KS, Henriksen OM, Law I, Bruun M, Simonsen AH, Mecocci P, Baroni M, Dottorini ME, Koikkalainen J, Lötjönen J, Hasselbalch SG. Evaluating 2-[ 18F]FDG-PET in differential diagnosis of dementia using a data-driven decision model. Neuroimage Clin 2020; 27:102267. [PMID: 32417727 DOI: 10.1016/j.nicl.2020.102267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 04/06/2020] [Accepted: 04/08/2020] [Indexed: 12/14/2022]
Abstract
Addition of 2-[18F]FDG-PET to common diagnostic tests improved the accuracy for DLB and FTD. Two new 2-[18F]FDG-PET biomarkers demonstrated specific disease patterns for DLB and FTD. Different combinations of diagnostic tests were valuable for each subtype of dementia.
2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography (2-[18F]FDG-PET) has an emerging supportive role in dementia diagnostic as distinctive metabolic patterns are specific for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). Previous studies have demonstrated that a data-driven decision model based on the disease state index (DSI) classifier supports clinicians in the differential diagnosis of dementia by using different combinations of diagnostic tests and biomarkers. Until now, this model has not included 2-[18F]FDG-PET data. The objective of the study was to evaluate 2-[18F]FDG-PET biomarkers combined with commonly used diagnostic tests in the differential diagnosis of dementia using the DSI classifier. We included data from 259 subjects diagnosed with AD, DLB, FTD, vascular dementia (VaD), and subjective cognitive decline from two independent study cohorts. We also evaluated three 2-[18F]FDG-PET biomarkers (anterior vs. posterior index (API-PET), occipital vs. temporal index, and cingulate island sign) to improve the classification accuracy for both FTD and DLB. We found that the addition of 2-[18F]FDG-PET biomarkers to cognitive tests, CSF and MRI biomarkers considerably improved the classification accuracy for all pairwise comparisons of DLB (balanced accuracies: DLB vs. AD from 64% to 77%; DLB vs. FTD from 71% to 92%; and DLB vs. VaD from 71% to 84%). The two 2-[18F]FDG-PET biomarkers, API-PET and occipital vs. temporal index, improved the accuracy for FTD and DLB, especially as compared to AD. Moreover, different combinations of diagnostic tests were valuable to differentiate specific subtypes of dementia. In conclusion, this study demonstrated that the addition of 2-[18F]FDG-PET to commonly used diagnostic tests provided complementary information that may help clinicians in diagnosing patients, particularly for differentiating between patients with FTD, DLB, and AD.
Collapse
|
17
|
Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, Urhemaa T, Tolonen A, van Gils M, Tong T, Guerrero R, Rueckert D, Dyremose N, Andersen BB, Simonsen AH, Lemstra A, Hallikainen M, Kurl S, Herukka SK, Remes AM, Waldemar G, Soininen H, Mecocci P, van der Flier WM, Lötjönen J, Hasselbalch SG. Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study. Curr Alzheimer Res 2020; 16:91-101. [PMID: 30605060 DOI: 10.2174/1567205016666190103152425] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/04/2018] [Accepted: 12/13/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. OBJECTIVES To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. METHODS In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. RESULTS In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). CONCLUSION Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians' confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
Collapse
Affiliation(s)
- Marie Bruun
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | - Kristian S Frederiksen
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | | | - Timo Urhemaa
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Tong Tong
- Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Ricardo Guerrero
- Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Daniel Rueckert
- Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom
| | - Nadia Dyremose
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | - Birgitte Bo Andersen
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | - Anja H Simonsen
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | - Afina Lemstra
- Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Merja Hallikainen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Sudhir Kurl
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Anne M Remes
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland and Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
| | - Gunhild Waldemar
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Neurocenter, Neurology, Kuopio University Hospital, Kuopio, Finland
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Wiesje M van der Flier
- Alzheimer Center, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | | | - Steen G Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, University of Copenhagen, Rigshospitalet, Denmark
| |
Collapse
|
18
|
Rhodius-Meester HFM, van Maurik IS, Koikkalainen J, Tolonen A, Frederiksen KS, Hasselbalch SG, Soininen H, Herukka SK, Remes AM, Teunissen CE, Barkhof F, Pijnenburg YAL, Scheltens P, Lötjönen J, van der Flier WM. Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy. PLoS One 2020; 15:e0226784. [PMID: 31940390 PMCID: PMC6961870 DOI: 10.1371/journal.pone.0226784] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/03/2019] [Indexed: 11/28/2022] Open
Abstract
INTRODUCTION An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.
Collapse
Affiliation(s)
- Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ingrid S van Maurik
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Kristian S Frederiksen
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Steen G Hasselbalch
- Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hilkka Soininen
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
| | - Anne M Remes
- Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
- Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
- MRC Oulu, Oulu University Hospital, Oulu, Finland
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
- Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| |
Collapse
|
19
|
Singh G, Vadera M, Samavedham L, Lim ECH. Multiclass Diagnosis of Neurodegenerative Diseases: A Neuroimaging Machine-Learning-Based Approach. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gurpreet Singh
- Dalio Institute of Cardiovascular Imaging, Weill Cornell Medicine, New York, New York 10021, United States
| | - Meet Vadera
- Department of Mechanical Engineering, Indian Institute of Technology Gandhinagar, Gandhinagar, Gujarat 382355, India
- Department of Computer Science, University of Massachusetts, Amherst, Massachusetts 01002, United States
| | | | | |
Collapse
|
20
|
Bruun M, Frederiksen KS, Rhodius-Meester HFM, Baroni M, Gjerum L, Koikkalainen J, Urhemaa T, Tolonen A, van Gils M, Rueckert D, Dyremose N, Andersen BB, Lemstra AW, Hallikainen M, Kurl S, Herukka SK, Remes AM, Waldemar G, Soininen H, Mecocci P, van der Flier WM, Lötjönen J, Hasselbalch SG. Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Alzheimers Res Ther 2019; 11:25. [PMID: 30894218 PMCID: PMC6425602 DOI: 10.1186/s13195-019-0482-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 03/11/2019] [Indexed: 12/19/2022]
Abstract
Background In clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics. Methods In this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy. Results After a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001). Conclusions Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications. Electronic supplementary material The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | | | - Timo Urhemaa
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Mark van Gils
- VTT Technical Research Centre of Finland Ltd, Tampere, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Nadia Dyremose
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Birgitte B Andersen
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Afina W Lemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Merja Hallikainen
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sudhir Kurl
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Sanna-Kaisa Herukka
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Anne M Remes
- Neurology, Neuro Center, Kuopio University Hospital, Kuopio, Finland.,Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Hilkka Soininen
- Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland.,Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | - Steen G Hasselbalch
- Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
| |
Collapse
|
21
|
Rhodius-Meester HFM, Liedes H, Koikkalainen J, Wolfsgruber S, Coll-Padros N, Kornhuber J, Peters O, Jessen F, Kleineidam L, Molinuevo JL, Rami L, Teunissen CE, Barkhof F, Sikkes SAM, Wesselman LMP, Slot RER, Verfaillie SCJ, Scheltens P, Tijms BM, Lötjönen J, van der Flier WM. Computer-assisted prediction of clinical progression in the earliest stages of AD. Alzheimers Dement (Amst) 2018; 10:726-736. [PMID: 30619929 PMCID: PMC6310913 DOI: 10.1016/j.dadm.2018.09.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Introduction Individuals with subjective cognitive decline (SCD) are at increased risk for clinical progression. We studied how combining different diagnostic tests can help to identify individuals who are likely to show clinical progression. Methods We included 674 patients with SCD (46% female, 64 ± 9 years, Mini-Mental State Examination 28 ± 2) from three memory clinic cohorts. A multivariate model based on the Disease State Index classifier incorporated the available baseline tests to predict progression to MCI or dementia over time. We developed and internally validated the model in one cohort and externally validated it in the other cohorts. Results After 2.9 ± 2.0 years, 151(22%) patients showed clinical progression. Overall performance of the classifier when combining cognitive tests, magnetic resonance imagining, and cerebrospinal fluid showed a balanced accuracy of 74.0 ± 5.5, with high negative predictive value (93.3 ± 2.8). Discussion We found that a combination of diagnostic tests helps to identify individuals at risk of progression. The classifier had particularly good accuracy in identifying patients who remained stable.
Collapse
Affiliation(s)
- Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Hilkka Liedes
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland
| | - Juha Koikkalainen
- VTT Technical Research Centre of Finland Ltd., Tampere, Finland.,Combinostics Ltd., Tampere, Finland
| | - Steffen Wolfsgruber
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Germany, and German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Nina Coll-Padros
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, IDIBAPS, Barcelona, Spain
| | - Johannes Kornhuber
- Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Oliver Peters
- Department of Psychiatry, Charité Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Frank Jessen
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Luca Kleineidam
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Germany, and German Center for Neurodegenerative Diseases, Bonn, Germany
| | - José Luis Molinuevo
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, IDIBAPS, Barcelona, Spain.,Barcelona Beta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's Disease and Other Cognitive Disorders Unit, Hospital Clínic, IDIBAPS, Barcelona, Spain
| | - Charlotte E Teunissen
- Neurochemistry Lab and Biobank, Department of Clinical Chemistry, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands and Institutes of Neurology and Healthcare Engineering, UCL, London, United Kingdom
| | - Sietske A M Sikkes
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Linda M P Wesselman
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Rosalinde E R Slot
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Sander C J Verfaillie
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Betty M Tijms
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands
| |
Collapse
|
22
|
Bruun M, Rhodius-Meester HFM, Koikkalainen J, Baroni M, Gjerum L, Lemstra AW, Barkhof F, Remes AM, Urhemaa T, Tolonen A, Rueckert D, van Gils M, Frederiksen KS, Waldemar G, Scheltens P, Mecocci P, Soininen H, Lötjönen J, Hasselbalch SG, van der Flier WM. Evaluating combinations of diagnostic tests to discriminate different dementia types. Alzheimers Dement (Amst) 2018; 10:509-518. [PMID: 30320203 PMCID: PMC6180596 DOI: 10.1016/j.dadm.2018.07.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Introduction We studied, using a data-driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.
Collapse
Affiliation(s)
- Marie Bruun
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Hanneke F M Rhodius-Meester
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | | | - Marta Baroni
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Le Gjerum
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Afina W Lemstra
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam Neuroscience, Amsterdam, the Netherlands.,UCL Institutes of Neurology and Healthcare Engineering, London, United Kingdom
| | - Anne M Remes
- Medical Research Center, Oulu University Hospital, Oulu, Finland.,Unit of Clinical Neuroscience, Neurology, University of Oulu, Oulu, Finland
| | - Timo Urhemaa
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Antti Tolonen
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Daniel Rueckert
- Department of Computing, Imperial College, London, United Kingdom
| | - Mark van Gils
- VTT Technical Research Center of Finland Ltd, Tampere, Finland
| | - Kristian S Frederiksen
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Gunhild Waldemar
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Philip Scheltens
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Patrizia Mecocci
- Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
| | - Hilkka Soininen
- Institute of Clinical Medicine, Neurology, University of Eastern Finland, Kuopio, Finland
| | | | - Steen G Hasselbalch
- Danish Dementia Research Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Wiesje M van der Flier
- Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, the Netherlands
| |
Collapse
|