Browsing Terveystieteiden tiedekunta by Subject "machine learning"

Now showing items 1-8 of 8

    • Automatic MRI Quantifying Methods in Behavioral-Variant Frontotemporal Dementia Diagnosis 

      Cajanus, A; Hall, A; Koikkalainen, J; Solje, E; Tolonen, A; Urhemaa, T; Liu, Y; Haanpää, RM; Hartikainen, P; Helisalmi, S; Korhonen, V; Rueckert, D; Hasselbalch, S; Waldemar, G; Mecocci, P; Vanninen, R; van Gils, M; Soininen, H; Lötjönen, J; Remes, AM (S. Karger AG, 2018)
      Aims: We assessed the value of automated MRI quantification methods in the differential diagnosis of behavioral-variant frontotemporal dementia (bvFTD) from Alzheimer disease (AD), Lewy body dementia (LBD), and subjective ...
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    • Comparison of feature representations in MRI-based MCI-to-AD conversion prediction 

      Gómez-Sancho, Marta; Tohka, Jussi; Gómez-Verdejo Vanessa (Elsevier BV, 2018)
      Alzheimer's disease (AD) is a progressive neurological disorder in which the death of brain cells causes memory loss and cognitive decline. The identification of at-risk subjects yet showing no dementia symptoms but who ...
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    • Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes 

      Lavikainen, Piia; Chandra, Gunjan; Siirtola, Pekka; Tamminen, Satu; Ihalapathirana, Anusha T; Röning, Juha; Laatikainen, Tiina; Martikainen, Janne (Dove Press Ltd, 2023)
      Purpose: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for ...

    • Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study 

      Spitzer, Hannah; Ripart, Mathilde; Whitaker, Kirstie; D'Arco, Felice; Mankad, Kshitij; Chen, Andrew A; Napolitano, Antonio; De Palma, Luca; De Benedictis, Alessandro; Foldes, Stephen; Humphreys, Zachary; Zhang, Kai; Hu, Wenhan; Mo, Jiajie; Likeman, Marcus; Davies, Shirin; Guttler, Christopher; Lenge, Matteo; Cohen, Nathan T; Tang, Yingying; et al. [Incl. Liu, Yawu; Kälviäinen, Reetta] (Oxford University Press on behalf of the Guarantors of Brain, 2022)
      One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural ...

    • Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects 

      Sarin, Jaakko K; Te Moller, Nikae Cr; Mohammadi, Ali; Prakash, Mithilesh; Torniainen, Jari; Brommer, Harold; Nippolainen, Ervin; Shaikh, Rubina; Mäkelä, Janne Ta; Korhonen, Rami K; René van Weeren, P; Afara, Isaac O; Töyräs, Juha (Elsevier BV, 2021)
      Objective To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. Method Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal ...
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    • Predicting Intelligence Based on Cortical WM/GM Contrast, Cortical Thickness and Volumetry 

      Valverde, JM; Imani, V; Lewis, JD; Tohka, J (Springer International Publishing, 2019)
      We propose a four-layer fully-connected neural network (FNN) for predicting fluid intelligence scores from T1-weighted MR images for the ABCD-challenge. In addition to the volumes of brain structures, the FNN uses cortical ...
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    • Quantitative Longitudinal Predictions of Alzheimer's Disease by Multi-Modal Predictive Learning 

      Prakash, Mithilesh; Abdelaziz, Mahmoud; Zhang, Linda; Strange, Bryan A; Tohka, Jussi; for the Alzheimer's Disease Neuroimaging Initiative (IOS Press, 2021)
      Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a ...
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    • The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up 

      Marinescu, Razvan V; Oxtoby, Neil P; Young, Alexandra L; Bron, Esther E; Toga, Arthur W; Weiner, Michael W; Barkhof, Frederik; Fox, Nick C; Eshaghi, Arman; Toni, Tina; Salaterski, Marcin; Lunina, Veronika; Ansart, Manon; Durrleman, Stanley; Lu, Pascal; Iddi, Samuel; Li, Dan; Thompson, Wesley K; Donohue, Michael C; Nahon, Aviv et al [incl. Tohka, Jussi; Ciszek, Robert] (MELBA, 2021)
      Accurate prediction of progression in subjects at risk of Alzheimer’s disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting ...
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