The DOI matches its Crossref record; 37 of the 38 references it cites were found in a registry or an index.
Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy
Abstract
Background Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP).Method Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP.Results The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP.Comparison with existing methods Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method.Conclusions The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Within-subject variability is usually excluded (Finn et al., 2015; Salvatore et al., 2014).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Salvatore et al., 2014
Citations
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
Staging of brain pathology related to sporadic Parkinson’s disease
Braak, H., Tredici, K. D., Rüb, U., de Vos, R. A. I., Jansen Steur, E. N. H., & Braak, E. (2003) · Neurobiology of Aging · doi:10.1016/s0197-4580(02)00065-9 · Cited by 10,777
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,420
Parkinson's disease: clinical features and diagnosis
Jankovic, J. (2008) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2007.131045 · Cited by 5,720
Diagnostic Criteria for Parkinson Disease
Gelb, D. J., Oliver, E., & Gilman, S. (1999) · Archives of Neurology · doi:10.1001/archneur.56.1.33 · Cited by 2,822
Stages in the development of Parkinson’s disease-related pathology
Braak, H., Ghebremedhin, E., Rüb, U., Bratzke, H., & Del Tredici, K. (2004) · Cell and Tissue Research · doi:10.1007/s00441-004-0956-9 · Cited by 2,689
Cited by
Sorted by citations
Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature
Mei, J., Desrosiers, C., & Frasnelli, J. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.633752 · Cited by 441
Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Mamun, S. A., & Mahmud, M. (2020) · Brain Informatics · doi:10.1186/s40708-020-00112-2 · Cited by 374
Deep learning based diagnosis of Parkinson’s disease using convolutional neural network
Sivaranjini, S., & Sujatha, C. M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7469-8 · Cited by 282
Salvatore, C., Cerasa, A., Battista, P., Gilardi, M. C., Quattrone, A., & Castiglioni, I. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00307 · Cited by 268
Self-citationKaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G. P., El–Sappagh, S., Islam, M. S., & Islam, S. M. R. (2020) · IEEE Access · doi:10.1109/access.2020.3042273 · Cited by 289
Radiological biomarkers for diagnosis in PSP: Where are we and where do we need to be?
Whitwell, J. L., Höglinger, G. U., Antonini, A., Bordelon, Y. M., Boxer, A. L., Colosimo, C., van Eimeren, T., Golbe, L. I., Kassubek, J., Kurz, C., et al. (2017) · Movement Disorders · doi:10.1002/mds.27038 · Cited by 263
Machine-Learning Methods for Computational Science and Engineering
Frank, M., Drikakis, D., & Charissis, V. (2020) · Computation · doi:10.3390/computation8010015 · Cited by 235
Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI
Shinde, S., Prasad, S., Saboo, Y., Kaushick, R., Saini, J. S., Pal, P. K., & Ingalhalikar, M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101748 · Cited by 230
Radiological images and machine learning: Trends, perspectives, and prospects
Zhang, Z., & Sejdić, E. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.02.017 · Cited by 249
Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I. (2015) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-015-2379-8 · Cited by 208
Self-citationArtificial intelligence as an emerging technology in the current care of neurological disorders
Patel, U. K., Anwar, A., Saleem, S., Malik, P., Rasul, B. M., Patel, K., Yao, R., Seshadri, A., Yousufuddin, M., & Arumaithurai, K. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09518-3 · Cited by 197
Structural neuroimaging as clinical predictor: A review of machine learning applications
Mateos-Pérez, J. M., Dadar, M., Lacalle-Aurioles, M., Iturria‐Medina, Y., Zeighami, Y., & Evans, A. C. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.08.019 · Cited by 191
Computer-Aided diagnosis of Parkinson’s Disease Using Enhanced Probabilistic Neural Network
Hirschauer, T. J., Adeli, H., & Buford, J. A. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0353-9 · Cited by 177
Analysis of in-air movement in handwriting: A novel marker for Parkinson's disease
Drotár, P., Mekyska, J., Rektorová, I., Masárová, L., Smékal, Z., & Faúndez-Zanuy, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.08.007 · Cited by 164
Neuroimaging of Parkinson's disease: Expanding views
Weingarten, C. P., Sundman, M. H., Hickey, P., & Chen, N. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.09.007 · Cited by 164
Complex networks reveal early MRI markers of Parkinson’s disease
Amoroso, N., Rocca, M. L., Monaco, A., Bellotti, R., & Tangaro, S. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.05.004 · Cited by 159
Tăuţan, A.-M., Ionescu, B. E., & Santarnecchi, E. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102081 · Cited by 156
A Comparative Study of Existing Machine Learning Approaches for Parkinson's Disease Detection
Pahuja, G., & Nagabhushan, T. N. (2018) · IETE Journal of Research · doi:10.1080/03772063.2018.1531730 · Cited by 148
Solana-Lavalle, G., & Rosas-Romero, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105793 · Cited by 143
Lahmiri, S., & Shmuel, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.029 · Cited by 137
Machine learning studies on major brain diseases: 5-year trends of 2014–2018
Sakai, K., & Yamada, K. (2018) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0794-4 · Cited by 127
Artificial intelligence-based diagnosis of Alzheimer's disease with brain MRI images
Yao, Z., Wang, H., Yan, W., Wang, Z. Q., Zhang, W., Wang, Z., & Zhang, G. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110934 · Cited by 135
Abós, A., Baggio, H. C., Segura, B., García-Díaz, A. I., Compta, Y., Martı́, M. J., Valldeoriola, F., & Junqué, C. (2017) · Scientific Reports · doi:10.1038/srep45347 · Cited by 122
Kellmeyer, P. (2018) · Neuroethics · doi:10.1007/s12152-018-9371-x · Cited by 121
Battista, P., Salvatore, C., & Castiglioni, I. (2017) · Behavioural Neurology · doi:10.1155/2017/1850909 · Cited by 116
Self-citationJoint feature-sample selection and robust diagnosis of Parkinson's disease from MRI data
Adeli, E., Shi, F., An, L., Chong-yaw Wee, A., Guorong Wu, A., Wang, T., & Shen, D. G. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.05.054 · Cited by 111
Diagnosis of Parkinson’s disease using deep CNN with transfer learning and data augmentation
Kaur, S., Aggarwal, H., & Rani, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10114-1 · Cited by 110
Zhang, J. (2022) · npj Parkinson s Disease · doi:10.1038/s41531-021-00266-8 · Cited by 109
The Impact of Artificial Intelligence in the Odyssey of Rare Diseases
Visibelli, A., Roncaglia, B., Spiga, O., & Santucci, A. (2023) · Biomedicines · doi:10.3390/biomedicines11030887 · Cited by 117
Peng, B., Wang, S., Zhou, Z., Liu, Y., Tong, B., Zhang, T., & Dai, Y. (2017) · Neuroscience Letters · doi:10.1016/j.neulet.2017.04.034 · Cited by 88
An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images
Kurmi, A., Biswas, S., Sen, S., Sinitca, A., Kaplun, D., & Sarkar, R. (2022) · Diagnostics · doi:10.3390/diagnostics12051173 · Cited by 87
Detection of Parkinson Disease in Brain MRI using Convolutional Neural Network
Shah, P. M., Zeb, A., Shafi, U., Zaidi, S. F. A., & Shah, M. A. (2018) · International Conference on Automation and Computing (ICAC) · doi:10.23919/iconac.2018.8749023 · Cited by 85
Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges
Ienca, M., & Ignatiadis, K. (2020) · AJOB Neuroscience · doi:10.1080/21507740.2020.1740352 · Cited by 87
Unified deep learning approach for prediction of Parkinson's disease
Wingate, J., Kollia, I., Bidaut, L. M., & Kollias, S. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.1526 · Cited by 7
Singh, G., & Lakshminarayanan, S. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.08.011 · Cited by 77
Enhancing Parkinson's Disease Prediction Using Machine Learning and Feature Selection Methods
Saeed, F., Al-Sarem, M., Almohaimeed, M., Emara, A.-H. M., Boulila, W., Alasli, M., & Ghabban, F. M. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.023124 · Cited by 74
Saravanan, S., Ramkumar, K., Adalarasu, K., Venkatesh, S., Kumar, S. R., Stalin, S., & Amirtharajan, R. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09710-1 · Cited by 74
A Secure Clinical Diagnosis With Privacy-Preserving Multiclass Support Vector Machine in Clouds
Zhang, M., Song, W., & Zhang, J. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3027758 · Cited by 67
Shcherbak, A., Kovalenko, E., & Somov, A. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3284944 · Cited by 75
A critical review of large language models: Sensitivity, bias, and the path toward specialized AI
Hajikhani, A., & Cole, C. H. (2024) · Quantitative Science Studies · doi:10.1162/qss_a_00310 · Cited by 79
Chougar, L., Faouzi, J., Pyatigorskaya, N., Yahia‐Cherif, L., Gaurav, R., Biondetti, E., Villotte, M., Valabrègue, R., Corvol, J., Brice, A., et al. (2020) · Movement Disorders · doi:10.1002/mds.28348 · Cited by 67
Machine Learning Interface for Medical Image Analysis
Zhang, Y. C., & Kagen, A. C. (2016) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-016-9910-0 · Cited by 67
Kaplan, E., Altunışık, E., Fırat, Y. E., Barua, P. D., Doğan, Ş., Bayğın, M., Demir, F. B., Tuncer, T., Palmer, E. E., Tan, R., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107030 · Cited by 66
Tagaris, A., Kollias, D., Stafylopatis, A., Tagaris, G. L., & Kollias, S. (2018) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213018500112 · Cited by 65
Efficient detection of Parkinson's disease using deep learning techniques over medical data
Sahu, L., Sharma, R., Sahu, I., Das, M., Sahu, B., & Kumar, R. (2021) · Expert Systems · doi:10.1111/exsy.12787 · Cited by 64
Wang, Z., Zhu, X., Adeli, E., Zhu, Y., Nie, F., Munsell, B. C., & Guorong Wu, A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.05.003 · Cited by 64
Automated methods for diagnosis of Parkinson’s disease and predicting severity level
Ayaz, Z., Naz, S., Khan, N. H., Razzak, I., & Imran, M. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06626-y · Cited by 68
Talai, A. S., Sedlacik, J., Boelmans, K., & Forkert, N. D. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.648548 · Cited by 64
Classification of Parkinson Disease Based on Patient’s Voice Signal Using Machine Learning
Ahmed, I., Aljahdali, S., Khan, M. S., & Kaddoura, S. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.022037 · Cited by 61
Rubbert, C., Mathys, C., Jockwitz, C., Hartmann, C. J., Eickhoff, S. B., Hoffstaedter, F., Caspers, S., Eickhoff, C. R., Sigl, B., Teichert, N., et al. (2019) · British Journal of Radiology · doi:10.1259/bjr.20180886 · Cited by 60
Deep Learning-based Early Parkinson’s Disease Detection from Brain MRI Image
Sangeetha, S., Baskar, K., Kalaivaani, P. C. D., & Kumaravel, T. (2023) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs56967.2023.10142754 · Cited by 59
Adeli, E., Guorong Wu, A., Saghafi, B., An, L., Shi, F., & Shen, D. G. (2017) · Scientific Reports · doi:10.1038/srep41069 · Cited by 59
Adaptive sparse learning using multi-template for neurodegenerative disease diagnosis
Lei, B., Zhao, Y., Huang, Z., Hao, X., Zhou, F., Elazab, A., Qin, J., & Lei, H. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101632 · Cited by 59
Kim, H., Lee, Y. I., Kim, Y., Lim, Y., Lee, J. S., Woo, J., Jang, S.-K., Oh, Y. J., Kim, H. W., Lee, E., et al. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.599042 · Cited by 60
Victor, E., Aghajan, Z. M., Sewart, A. R., & Christian, R. (2019) · Psychological Assessment · doi:10.1037/pas0000724 · Cited by 57
Castillo-Barnés, D., Ramı́rez, J., Segovia, F., Martínez-Murcia, F. J., Salas-Gonzalez, D., & Górriz, J. M. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00053 · Cited by 55
The Role of Magnetic Resonance Imaging for the Diagnosis of Atypical Parkinsonism
Chougar, L., Pyatigorskaya, N., Degos, B., Grabli, D., & Lehéricy, S. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00665 · Cited by 57
Haq, A. U., Li, J., Agbley, B. L. Y., Mawuli, C. B., Ali, Z., Nazir, S., & Din, S. U. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118045 · Cited by 63
Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks
Kollia, I., Stafylopatis, A., & Kollias, S. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851995 · Cited by 53
Discriminative Analysis of Parkinson’s Disease Based on Whole-Brain Functional Connectivity
Chen, Y., Yang, W., Long, J., Zhang, Y., Feng, J., Li, Y., & Huang, B. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124153 · Cited by 50
Neuroimage analysis using artificial intelligence approaches: a systematic review
Bacon, E. J., He, D., Achi, N. A. D., Wang, L., Li, H., Yao-Digba, P. D. Z., Monkam, P., & Qi, S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03097-w · Cited by 52
Xu, J., & Zhang, M. (2019) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.9b00207 · Cited by 48
Cigdem, O., & Demirel, H. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2018.08.017 · Cited by 46
Özkan, Ç., Beheshti, I., & Demirel, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.006 · Cited by 45
Meijer, F. J. A., van Rumund, A., Tuladhar, A. M., Aerts, M. B., Titulaer, I., Esselink, R. A. J., Bloem, B. R., Verbeek, M. M., & Goraj, B. M. (2015) · Neuroradiology · doi:10.1007/s00234-015-1515-7 · Cited by 44
Structural Imaging in Parkinson’s Disease: New Developments
Prange, S., Météreau, É., & Thobois, S. (2019) · Current Neurology and Neuroscience Reports · doi:10.1007/s11910-019-0964-5 · Cited by 45
Monroy, G. L., Won, J., Dsouza, R., Pande, P., Hill, M. C., Porter, R. G., Novak, M. A., Spillman, D. R., & Boppart, S. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0094-0 · Cited by 43
Graph theoretical metrics and machine learning for diagnosis of Parkinson's disease using rs-fMRI
Kazeminejad, A., Golbabaei, S., & Hamid Soltanian-zadeh, A. (2017) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp.2017.8324124 · Cited by 42
End-to-End Parkinson Disease Diagnosis using Brain MR-Images by 3D-CNNPreprint
Esmaeilzadeh, S., Yang, Y., & Adeli, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.05233 · Cited by 42
Dennis, A.-G. P., & Strafella, A. P. (2024) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2024.106986 · Cited by 41
Progressive supranuclear palsy
Giagkou, N., Höglinger, G. U., & Stamelou, M. (2019) · International review of neurobiology · doi:10.1016/bs.irn.2019.10.013 · Cited by 44
Erdaş, Ç. B., & Sümer, E. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1485 · Cited by 39
Recent imaging advances in neurology
Rocchi, L., Niccolini, F., & Politis, M. (2015) · Journal of Neurology · doi:10.1007/s00415-015-7711-x · Cited by 40
Vasta, R., Cerasa, A., Sarica, A., Bartolini, E., Martino, I., Mari, F., Metitieri, T. T., Quattrone, A., Gambardella, A., Guerrini, R., et al. (2018) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2018.09.008 · Cited by 38
Self-citationVeetil, I. K., Gopalakrishnan, E. A., Sowmya, V., & Soman, K. P. (2021) · IEEE India Conference · doi:10.1109/indicon52576.2021.9691745 · Cited by 39
Future of Machine Learning ( ML ) and Deep Learning ( DL ) in Healthcare Monitoring System
Kumar, K., Chaudhury, K., & Tripathi, S. L. (2022) · Wiley eBooks · doi:10.1002/9781119861850.ch17 · Cited by 41
Classification of Parkinson’s Disease in Patch-Based MRI of Substantia Nigra
Hussain, S. S., Xu, D., Shah, P. M., Islam, S. U., Alam, M., Khan, I. A., Awwad, F. A. A., & Ismail, E. A. A. (2023) · Diagnostics · doi:10.3390/diagnostics13172827 · Cited by 36
Clinical Application of Brain MRI in the Diagnostic Work-up of Parkinsonism
Meijer, F. J. A., Goraj, B. M., Bloem, B. R., & Esselink, R. A. J. (2017) · Journal of Parkinson s Disease · doi:10.3233/jpd-150733 · Cited by 36
Chen, Y., Zhu, G., Liu, D., Liu, Y., Yuan, T., Zhang, X., Jiang, Y., Du, T., & Zhang, J. (2020) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2020.116721 · Cited by 36
Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism
Segovia, F., Górriz, J. M., Ramı́rez, J., Martínez-Murcia, F. J., Levin, J., Schuberth, M., Brendel, M., Rominger, A., Bötzel, K., Garraux, G., et al. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00023 · Cited by 36
Mohammadi, A. G., Mehralian, P., Naseri, A., & Sajedi, H. (2021) · Array · doi:10.1016/j.array.2021.100079 · Cited by 35
Deep learning based diagnosis of Parkinson’s Disease using diffusion magnetic resonance imaging
Zhao, H., Tsai, C., Zhou, M., Liu, Y., Chen, Y.-L., Huang, F., Lin, Y., & Wang, J. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00631-y · Cited by 38
Sarica, A., Quattrone, A., & Quattrone, A. (2022) · Brain Imaging and Behavior · doi:10.1007/s11682-022-00688-9 · Cited by 37
Self-citationA wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer’s disease
Salvatore, C., & Castiglioni, I. (2018) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.12.016 · Cited by 33
Self-citationWidespread diffusion changes differentiate Parkinson's disease and progressive supranuclear palsy
Talai, A. S., Sedlacik, J., Boelmans, K., & Forkert, N. D. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.09.028 · Cited by 33
Veetil, I. K., Chowdary, D. E., Chowdary, P. N., Sowmya, V., & Gopalakrishnan, E. A. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104407 · Cited by 33
Salari, N., Kazeminia, M., Sagha, H., Daneshkhah, A., Ahmadi, A., & Mohammadi, M. (2022) · Current Psychology · doi:10.1007/s12144-022-02949-8 · Cited by 32
Cerasa, A., Castiglioni, I., Salvatore, C., Funaro, A., Martino, I., Alfano, S., Donzuso, G., Perrotta, P., Gioia, M. C., Gilardi, M. C., et al. (2015) · Behavioural Neurology · doi:10.1155/2015/924814 · Cited by 29
Self-citationResearch on the Aided Diagnosis Method of Diseases Based on Domain Semantic Knowledge Bases
Chen, D., Zhao, H., & Zhang, X. (2019) · IEEE Access · doi:10.1109/access.2018.2875722 · Cited by 29
Rana, A., Dumka, A., Singh, R., Panda, M. K., & Priyadarshi, N. (2022) · Diagnostics · doi:10.3390/diagnostics12112708 · Cited by 32
Rana, B., Juneja, A., Saxena, M., Gudwani, S., Kumaran, S. S., Behari, M., & Agrawal, R. K. (2015) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22141 · Cited by 29
Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty
Chougar, L., Faucher, A., Faouzi, J., Lejeune, F., Lobo, G. G., Jovanović, Č., Cormier‐Dequaire, F., Dupont, G., Vidailhet, M., Corvol, J., et al. (2024) · Movement Disorders · doi:10.1002/mds.29760 · Cited by 28
On the review of image and video-based depression detection using machine learning
Ashraf, A., Gunawan, T. S., Riza, B. S., Haryanto, E. V., & Janin, Z. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v19.i3.pp1677-1684 · Cited by 33
Liu, Y., Xiao, B., Zhang, C., Li, J., Lai, Y., Shi, F., Shen, D. G., Wang, L., Sun, B., Li, Y., et al. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.731109 · Cited by 28
Singh, G., Vadera, M. P., Lakshminarayanan, S., & Lim, E. C. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.07.331 · Cited by 28
Parkinson’s Disease Detection Using Ensemble Architecture from MR Images
Mostafa, T. A., & Cheng, I. (2020) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe50027.2020.00167 · Cited by 27
Zhang, X., Cao, X., Xue, C., Zheng, J., Zhang, S., Huang, Q., & Liu, W. (2021) · Brain and Behavior · doi:10.1002/brb3.2103 · Cited by 27
Show me your smartphone… and then I will show you your brain structure and brain function
Montag, C., Elhai, J. D., & Dagum, P. (2021) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.272 · Cited by 27
Patra, A. K., Ray, R., Abdullah, A. A., & Dash, S. R. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1372/1/012041 · Cited by 26
Sparse feature learning for multi-class Parkinson’s disease classification
Lei, H., Zhao, Y., Wen, Y., Luo, Q., Cai, Y., Liu, G., & Lei, B. (2018) · Technology and Health Care · doi:10.3233/thc-174548 · Cited by 26
Metadata
- Authors
- Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M. C., & Quattrone, A.
- Affiliations
- University of Milano-Bicocca (IT)
- Institute of Neurological Sciences (IT)
- Institute of Molecular Bioimaging and Physiology (IT)
- Magna Graecia University (IT)
- Corresponding author
- Castiglioni, I.
- ORCID
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 222
- Pages
- 230-237
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2037984755
- Cited by
- 299
- References
- 38
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 25 · 2023: 36 · 2022: 41 · 2021: 24 · 2020: 37 · 2019: 23 · 2018: 27 · 2017: 24 · 2016: 5 · 2015: 15 · 2014: 1
- FWCI
- 4.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Health Sciences › Medicine › Clinical Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Parkinson's disease, progressive supranuclear palsy, differential diagnosis, brain MRI, voxel-based morphometry, computer-aided diagnosis, T1-weighted imaging, support vector machine, principal component analysis, midbrain, thalamus, passive optical networks, corpus callosum
- MeSH terms
- Aged, Algorithms, Artificial Intelligence, Brain, Corpus Callosum, Diagnosis, Differential, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Mesencephalon, Parkinson Disease, Pons, Retrospective Studies, Sensitivity and Specificity, Supranuclear Palsy, Progressive, Thalamus, Principal Component Analysis, Support Vector Machine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
