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Tiivistelmä. Itsemyötätunto tarkoittaa kärsimyksen huomaamista itsessä sekä halua ja pyrkimystä lievittää kärsimystä. Vaikka itsemyötätunnon hyödyistä mielenterveydelle on jo paljon näyttöä, on taustalla oleva mekanismi edelleen tuntematon. Itsemyötätunnon vaikutusta tunteiden säätelyyn on ehdotettu yhdeksi mahdolliseksi mekanismiksi itsemyötätunnon ja mielenterveyden välillä. Tavoitteena tässä kandidaatintutkielmassa oli tarkastella, miten itsemyötätunto on yhteydessä tunteiden säätelyyn. Lisäksi tavoitteena oli tarkastella, onko itsemyötätunto yhteydessä mielenterveyden oireisiin ja mielenterveyden häiriöihin, erityisesti depressioon ja ahdistuneisuushäiriöihin, tunteiden säätelyn kautta. Menetelmänä käytettiin kuvailevaa kirjallisuuskatsausta. Kirjallisuuskatsaukseen koottiin tuoreita, vertaisarvioituja tutkimuksia, joissa oli tutkittu itsemyötätunnon ja tunteiden säätelyn yhteyttä ja/tai tunteiden säätelyä välittävänä tekijänä itsemyötätunnon ja mielenterveyden oireiden välillä. Kirjallisuuskatsauksen perusteella itsemyötätunto on negatiivisessa yhteydessä useiden epäadaptiivisten tunteiden säätelystrategioiden käytön kanssa ja positiivisessa yhteydessä joidenkin adaptiivisten tunteiden säätelystrategioiden käytön kanssa. Itsemyötätunto on myös negatiivisessa yhteydessä erilaisiin vaikeuksiin tunteiden säätelyssä, kuten vaikeuksiin tunteiden tiedostamisessa, erottamisessa, hyväksymisessä ja tehokkaiden tunteiden säätelystrategioiden saavuttamisessa. Lisäksi itsemyötätunto on yhteydessä korkeampaan sykevälivaihteluun, minkä on esitetty olevan tunteiden säätelykykyä heijastava fysiologinen indeksi. Kirjallisuuskatsauksessa saatiin myös viitteitä siitä, että itsemyötätunto olisi yhteydessä mielenterveyden oireisiin joidenkin tunteiden säätelystrategioiden kautta. Suurin osa kirjallisuuskatsaukseen kootuista tutkimuksista on kuitenkin poikkileikkaustutkimuksia, mikä rajoittaa kausaalisuuspäätelmiä. Lisäksi suurimmassa osassa tutkimuksista koehenkilöinä on ollut opis

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusion: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Objectives: The KNee OsteoArthritis Prediction (KNOAP2020) challenge was organized to objectively compare methods for the prediction of incident symptomatic radiographic knee osteoarthritis within 78 months on a test set with blinded ground truth. Design: The challenge participants were free to use any available data sources to train their models. A test set of 423 knees from the Prevention of Knee Osteoarthritis in Overweight Females (PROOF) study consisting of magnetic resonance imaging (MRI) and X-ray image data along with clinical risk factors at baseline was made available to all challenge participants. The ground truth outcomes, i.e., which knees developed incident symptomatic radiographic knee osteoarthritis (according to the combined ACR criteria) within 78 months, were not provided to the participants. To assess the performance of the submitted models, we used the area under the receiver operating characteristic curve (ROCAUC) and balanced accuracy (BACC). Results: Seven teams submitted 23 entries in total. A majority of the algorithms were trained on data from the Osteoarthritis Initiative. The model with the highest ROCAUC (0.64 (95% confidence interval (CI): 0.57–0.70)) used deep learning to extract information from X-ray images combined with clinical variables. The model with the highest BACC (0.59 (95% CI: 0.52–0.65)) ensembled three different models that used automatically extracted X-ray and MRI features along with clinical variables. Conclusions: The KNOAP2020 challenge established a benchmark for predicting incident symptomatic radiographic knee osteoarthritis. Accurate prediction of incident symptomatic radiographic knee osteoarthritis is a complex and still unsolved problem requiring additional investigation.

Finite element (FE) modeling is becoming an increasingly popular method for analyzing knee joint mechanics and biomechanical mechanisms leading to osteoarthritis (OA). The most common and widely available imaging method for knee OA diagnostics is planar X-ray imaging, while more sophisticated imaging methods, e.g., magnetic resonance imaging (MRI) and computed tomography (CT), are seldom used. Hence, the capability to produce accurate biomechanical knee joint models directly from X-ray imaging would bring FE modeling closer to clinical use. Here, we extend our atlas-based framework by generating FE knee models from X-ray images (N = 28). Based on measured anatomical landmarks from X-ray and MRI, knee joint templates were selected from the atlas library. The cartilage stresses and strains of the X-ray-based model were then compared with the MRI-based model during the stance phase of the gait. The biomechanical responses were statistically not different between MRI- vs. X-ray-based models when the template obtained from X-ray imaging was the same as the MRI template. However, if this was not the case, the peak values of biomechanical responses were statistically different between X-ray and MRI models. The developed X-ray-based framework may pave the way for a clinically feasible approach for knee joint FE modeling.

Correction to: Jahangir, S., Mohammadi, A., Mononen, M.E. et al. Rapid X-Ray-Based 3-D Finite Element Modeling of Medial Knee Joint Cartilage Biomechanics During Walking. Ann Biomed Eng 50, 666–679 (2022). https://doi.org/10.1007/s10439-022-02941-0 Rinnakkaistallennettu versio / Self-archived version

Summary: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images. Introduction: In this study, we aimed to investigate the applicability of deep learning (DL) to assess radiographic hip osteoarthritis (rHOA) on computed tomography (CT). Methods: The study data consisted of 94 abdominopelvic clinical CTs and 5659 hip X-ray images collected from Cohort Hip and Cohort Knee (CHECK). The CT slices were sequentially summed to create radiograph-like 2-D images named CT-AP. X-ray and CT-AP images were classified as rHOA if they had osteoarthritic changes corresponding to Kellgren-Lawrence grade 2 or higher. The study data was split into 55% training, 30% validation, and 15% test sets. A pretrained ResNet18 was optimized for a classification task of rHOA vs. no-rHOA. Five models were trained using (1) X-rays, (2) downsampled X-rays, (3) combination of CT-AP and X-ray images, (4) combination of CT-AP and downsampled X-ray images, and (5) CT-AP images. Results: Amongst the five models, Model-3 and Model-5 performed best in detecting rHOA from the CT-AP images. Model-3 detected rHOA on the test set of CT-AP images with a balanced accuracy of 82.2% and was able to discriminate rHOA from no-rHOA with an area under the receiver operating characteristic curve (ROC AUC) of 0.93 [0.75–0.99]. Model-5 detected rHOA on the test set at a balanced accuracy of 82.2% and classified rHOA from no-rHOA with an ROC AUC of 0.89 [0.67–0.97]. Conclusion: CT-based summation images that resemble radiographs can be used to detect rHOA. In addition, in the absence of large training data, a reliable DL model can be optimized by combining CT-AP and X-ray images.