Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data

Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.Comment: 22 pages, 12 figures, 10 table

Polvinivelrikko ja sen toteamiseen sekä etenemisen ennustamiseen kehitetyt tekoälysovellukset

Tiivistelmä. Nivelrikko on maailman yleisin tuki- ja liikuntaelimistön sairaus, joka aiheuttaa yksilötasolla paljon toimintakyvyn alenemaa, kipua sekä yhteiskunnallisesti suuria sosioekonomisia kustannuksia. Nivelrikko affisioi erityisesti kättä, lonkkaa ja polvea. Nivelrikko on koko nivelen tauti, jota yleisesti kuvataan nivelruston ja siihen liittyvien kudosten etenevänä rappeutumana ja degeneraationa. Polvinivelrikon diagnoosi voidaan asettaa kliinisin perustein ja tarvittaessa kuvantamistutkimuksia käytetään diagnoosin vahvistamisessa. Kuvantamistutkimuksista natiiviröntgentutkimus on ensisijainen ja sen avulla pystytään asettamaan polvinivelrikon radiologinen vaikeusaste. Nivelrikon etiologia ja patogeneesi ovat vielä huonosti ymmärrettyjä, mikä puolestaan tekee taudin ennustamisesta haasteellista perinteisillä menetelmillä kuten kliinisellä arviolla tai kuvantamistutkimuksilla. Tämä viittaa vahvasti tarpeeseen kehitellä uusia työkaluja polvinivelrikon varhaiseen tunnistamiseen ja prognoosin luomiseen. Tietokoneiden laskentatehon lisääntymisen myötä on viime vuosikymmenen aikana kehitelty uusia tekoälysovelluksia hyödyntäen koneoppimismenetelmiä, joiden kehittyneempi muoto on syväoppiminen. Näiden avulla on pystytty automaattisesti luomaan arvio polvinivelrikon vakavuusasteesta ja jopa tarjoamaan ennuste taudin etenemisestä. Tämä tutkielma keskittyy kuvaamaan polvinivelrikon diagnostiikkaan ja ennusteen määrittämiseen käytettyjä uusia syväoppimismenetelmällä ohjelmoituja tekoälysovelluksia. Erityisesti tutkielmassa käsitellään Oulun yliopistossa polvinivelrikon diagnostiikkaan ja prognoosiin kehiteltyjä uusia tekoälysovelluksia, jotka ovat alallansa uraauurtavia. Tutkielmassa pohditaan ja esitetään, kuinka näitä sekä muita aiemmin kehiteltyjä tekoälyohjelmia voitaisiin integroida kliiniseen kontekstiin yleis- ja erikoislääkäreiden työssä ja kuinka niillä on mahdollisuus parantaa polvinivelrikosta kärsivien potilaiden hoidonsuunnittelua ja ennaltaehkäisyä

Accurate, Fast and Controllable Image and Point Cloud Registration

Registration is the process of establishing spatial correspondences between two objects. Many downstream tasks, e.g, in image analysis, shape animation, can make use of these spatial correspondences. A variety of registration approaches have been developed over the last decades, but only recently registration approaches have been developed that make use of and can easily process the large data samples of the big data era. On the one hand, traditional optimization-based approaches are too slow and cannot take advantage of very large data sets. On the other hand, registration users expect more controllable and accurate solutions since most downstream tasks, e.g., facial animation and 3D reconstruction, increasingly rely on highly precise spatial correspondences. In recent years, deep network registration approaches have become popular as learning-based approaches are fast and can benefit from large-scale data during network training. However, how to make such deep-learning-based approached accurate and controllable is still a challenging problem that is far from being completely solved. This thesis explores fast, accurate and controllable solutions for image and point cloud registration. Specifically, for image registration, we first improve the accuracy of deep-learning-based approaches by introducing a general framework that consists of affine and non-parametric registration for both global and local deformation. We then design a more controllable image registration approach that image regions could be regularized differently according to their local attributes. For point cloud registration, existing works either are limited to small-scale problems, hardly handle complicated transformations or are slow to solve. We thus develop fast, accurate and controllable solutions for large-scale real-world registration problems via integrating optimal transport with deep geometric learning.Doctor of Philosoph

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe