Theme II
Theme II, dealing with data-driven solutions for healthcare, combines expertise from TAU, UO, and VTT in a balanced manner, with joint participation to all of its four workpackages. The PIs include both technology and clinical experts – they are all highly recognized at global level in their respective fields.
Each PI represents teams that have been working with health data analytics for healthcare challenges for over 30 years, as demonstrated e.g. by strong joint publications and co-ordinating roles in research project consortia. They range from FP4 to H2020 project co-ordination in EU context and from early TEKES research, SHOKs to large BF co-innovation efforts, as well as AoF consortia leadership in Finnish projects.
From a technological excellence point of view, spearheads are in (biomedical) signal and image processing, AI and machine learning for (clinical) decision support with an emphasis on real-life demands and special strengths in combining different data sources and modalities.
From a clinical need point of view, demonstrated top expertise exists in the high-need domains of chronic musculoskeletal disorders, cardiovascular, and neurodegenerative diseases.
The researcher expertise and experience are complemented by highly valuable patient data cohorts at the sites, fast large computing research infrastructures and own health-technology test labs.
Selected references
WP1: Solutions for Chronic Musculoskeletal Conditions
Bayramoglu N, Tiulpin A, Hirvasniemi J, Nieminen MT, Saarakkala S. Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis. Osteoarthritis Cartilage. 2020 Jul;28(7):941-952
Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, Hoy D, Karppinen J, Pransky G, Sieper J, Smeets RJ, Underwood M; Lancet Low Back Pain Series Working Group. What low back pain is and why we need to pay attention. Lancet. 2018 Jun 9;391(10137):2356-2367
Lehtovirta S, Mäkitie RE, Casula V, Haapea M, Niinimäki J, Niinimäki T, Peuna A, Lammentausta E, Mäkitie O, Nieminen MT. Defective WNT signaling may protect from articular cartilage deterioration – a quantitative MRI study on subjects with a heterozygous WNT1 mutation. Osteoarthritis Cartilage, 27:1636-1646, 2019.
Nguyen HH, Saarakkala S, Blaschko MB, Tiulpin A. Semixup: In-and Out-of-Manifold Regularization for Deep Semi-Supervised Knee Osteoarthritis Severity Grading from Plain Radiographs. IEEE Trans Med Imaging. 2020 Aug 17;PP. doi: 10.1109/TMI.2020.3017007
Nieminen MT, Casula V, Nevalainen MT, Saarakkala S. Osteoarthritis year in review 2018: imaging. Osteoarthritis Cartilage. 2019 Mar;27(3):401-411
Saukkonen J, Määttä J, Oura P, Kyllönen E, Tervonen O, Niinimäki J, Auvinen J, Karppinen J. Association Between Modic Changes and Low Back Pain in Middle Age: A Northern Finland Birth Cohort Study. Spine (Phila Pa 1976). 2020 Apr 23. doi: 10.1097/BRS.000000000000352 State of the Art: Imaging of Osteoarthritis-Revisited 2020.
Roemer FW, Demehri S, Omoumi P, Link TM, Kijowski R, Saarakkala S, Crema MD, Guermazi A. Radiology. 2020 Jul;296(1):5-21
Tiulpin A, Klein S, Bierma-Zeinstra SMA, Thevenot J, Rahtu E, Meurs JV, Oei EHG, Saarakkala S. Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data. Sci Rep. 2019 Dec 27;9(1):20038.
Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Sci Rep. 2018 Jan 29;8(1):1727.
WP2: Solutions for Cardiovascular diseases
Cerdán de Las Heras J, Tulppo M, Kiviniemi AM, Hilberg O, Løkke A, Ekholm S, Catalán-Matamoros D, Bendstrup E. Augmented reality glasses as a new tele-rehabilitation tool for home use: patients’ perception and expectations. Disabil Rehabil Assist Technol. 2020 Aug 4:1-7. doi: 10.1080/17483107.2020.1800111
Hassani-Nezhad-Gashti F, Salonurmi T, Hautajärvi H, Rysä J, Hakkola J, Hukkanen J. Clin Pharmacol Ther. 2020. Pregnane X Receptor Activator Rifampin Increases Blood Pressure and Stimulates Plasma Renin Activity.
Hautajärvi H, Hukkanen J, Turpeinen M, Mattila S, Tolonen A. Quantitative analysis of 4β- and 4α‑hydroxycholesterol in human plasma and serum by UHPLC/ESI-HR-MS. J Chromatogr B Analyt Technol Biomed Life Sci. 2018 Nov 15;1100-1101:179-186.
Hintsala HE, Kiviniemi AM, Antikainen R, Mäntysaari M, Jokelainen J, Hassi J, Tulppo MP, Herzig KH, Keinänen-Kiukaanniemi S, Rintamäki H, Jaakkola JJK, Ikäheimo TM. High Home Blood Pressure Variability Associates With Exaggerated Blood Pressure Response to Cold Stress. Am J Hypertens. 2019 May 9;32(6):538-546. doi: 10.1093/ajh/hpz011
Kiviniemi AM, Hautala AJ, Karjalainen JJ, Piira OP, Lepojärvi S, Ukkola O, Huikuri HV, Tulppo MP. Acute post-exercise change in blood pressure and exercise training response in patients with coronary artery disease. Front Physiol. 2015 Jan 12;5:526. doi: 10.3389/fphys.2014.00526. eCollection 2014
Kiviniemi AM, Kenttä TV, Lepojärvi S, Perkiömäki JS, Piira OP, Ukkola O, Huikuri HV, Junttila MJ, Tulppo MP. Recovery of rate-pressure product and cardiac mortality in coronary artery disease patients with type 2 diabetes. Diabetes Res Clin Pract. 2019 Apr;150:150-157. doi: 10.1016/j.diabres.2019.03.007. Epub 2019 Mar 11
Seo YG, Salonurmi T, Jokelainen T, Karppinen P, Teeriniemi AM, Han J, Park KH, Oinas-Kukkonen H, Savolainen MJ. Lifestyle counselling by persuasive information and communications technology reduces prevalence of metabolic syndrome in a dose-response manner: a randomized clinical trial (PrevMetSyn). Ann Med. 2020 Sep;52(6):321-330. doi: 10.1080/07853890.2020.1783455.
Teeriniemi AM, Salonurmi T, Jokelainen T, Vähänikkilä H, Alahäivälä T, Karppinen P, Enwald H, Huotari ML, Laitinen J, Oinas-Kukkonen H, Savolainen MJ. A randomized clinical trial of the effectiveness of a Web-based health behaviour change support system and group lifestyle counselling on body weight loss in overweight and obese subjects: 2-year outcomes. J Intern Med. 2018 Nov;284(5):534-545.
Tulppo MP, Huikuri HV, Mäkikallio TH, Seppänen T, Airaksinen KEJ: Heart rate dynamics during accentuated sympathovagal interaction. Am J Physiol. 274: H810-H816, 1998.
Tulppo MP, Kiviniemi AM, Kallio M, Hautala AJ, Pietarila P, Seppänen T, Mäkikallio TH, Huikuri HV. Physiological background of the loss of fractal heart rate dynamics. Circulation, Jul. 19;112(3):314-9, 2005
Tulppo MP, Kiviniemi AM, Lahtinen M, Ukkola O, Toukola T, Perkiömäki J, Junttila1 MJ, Huikuri HV. Physical Activity and the Risk for Sudden Cardiac Death in Patients with Coronary Artery Disease. Circulation A.E. 2020 Jun;13(6):e007908. doi: 10.1161/CIRCEP.119.007908.
WP3: Solutions for Neurodegenerative diseases
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 S-K, 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. Alzheimer’s research & therapy, 11, 1, 25, 2019. doi.org/10.1186/s13195-019-0482-3.
Hall A, Pekkala T, Polvikoski T, van Gils M, Kivipelto M, Lötjönen J, Mattila J, Kero M, Myllykangas L, Mäkelä M, Oinas M, Paetau A, Soininen H, Tanskanen M, Solomon A. Prediction models for dementia and neuropathology in the oldest old: the Vantaa 85+ cohort study. Alzheimer’s Research & Therapy, 11(11), 2019. doi.org/10.1186/s13195-018-0450-3
Koikkalainen J, Pölönen H, Mattila J, van Gils M, Soininen H, Lötjönen J. Improved Classification of Alzheimer’s Disease Data via Removal of Nuisance Variability. PloS one, 7(2), 2012.
Smits EJ, Tolonen AJ, Cluitmans L, van Gils M, Zietsma RC, Borgemeester RWK, van Laar T, Maurits NM. Graphical Tasks to Measure Upper Limb Function in Patients With Parkinson’s Disease: Validity and Response to Dopaminergic Medication. IEEE Journal of Biomedical and Health Informatics, 99, 2015.
WP4: Grand Challenges for data analytics in real-life healthcare
Ermes M, Pärkkä J, Mäntyjärvi J, Korhonen I. Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions. IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 1, pp. 20-26, Jan. 2008, doi: 10.1109/TITB.2007.899496.
Kiranyaz S, Ince T, Gabbouj M. Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias. Scientific Reports, vol. 7, no. 9270, 24 August 2017, DOI 10.1038/s41598-017-09544-z (SREP-16-52549-T) (rdcu.be/vfYE).
Kiranyaz S, Ince T, Gabbouj M. Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, DOI: 10.1109/TBME.2015.2468589, vol. 63, no. 3, pp. 664 – 675, 2016.
Kiranyaz S, Malik J, Abdallah HB, Ince T, Iosifidis A, Gabbouj M. Self-Organized Operational Neural Networks with Generative Neurons. http://arxiv.org/abs/2004.11778.
Kiranyaz S, Zabihi M, Rad AB, Ince T, Hamila R, Gabbouj M. Real-time Phonocardiogram Anomaly Detection by Adaptive 1D Convolutional Neural Networks. Neurocomputing, 19 May 2020, https://doi.org/10.1016/j.neucom.2020.05.063.
Mattila J, Koikkalainen J, Virkki A, van Gils M, Lötjönen J; for the Alzheimer’s Disease Neuroimaging Initiative. Design and Application of a Generic Clinical Decision Support System for Multi-Scale Data. IEEE Transactions on Biomedical Engineering, 59(1) 234-40, 2012.
Tran D, Kiranyaz S, Gabbouj M, Iosifidis A. Heterogeneous Multilayer Generalized Operational Perceptron. IEEE Transactions on Neural Networks and Learning Systems, (early access May 31, 2019), vol. 31, no. 3, March 2020, DOI: 10.1109/TNNLS.2019.2914082, pp. 710-724.
What is FinMedTechNet?
FinMedTechNet is a joint initiative among two Finnish universities – Tampere University and University of Oulu – and VTT Technical Research Centre of Finland Ltd. to collaborate in research and in commercialization activities in the medical technology field.
What do we do?
We develop action models and supporting activities for researchers, and improve the communication among all the stakeholders, including local university hospitals, health hubs and districts as well as international competence centers and companies.