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607 results on '"Tervonen, Osmo"'

1. Using artificial intelligence to detect chest X-rays with no significant findings in a primary health care setting in Oulu, Finland

Author

Keski-Filppula, Tommi, Nikki, Marko, Haapea, Marianne, Ramanauskas, Naglis, and Tervonen, Osmo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Objectives: To assess the use of artificial intelligence-based software in ruling out chest X-ray cases, with no significant findings in a primary health care setting. Methods: In this retrospective study, a commercially available artificial intelligence (AI) software was used to analyse 10 000 chest X-rays of Finnish primary health care patients. In studies with a mismatch between an AI normal report and the original radiologist report, a consensus read by two board-certified radiologists was conducted to make the final diagnosis. Results: After the exclusion of cases not meeting the study criteria, 9579 cases were analysed by AI. Of these cases, 4451 were considered normal in the original radiologist report and 4644 after the consensus reading. The number of cases correctly found nonsignificant by AI was 1692 (17.7% of all studies and 36.4% of studies with no significant findings). After the consensus read, there were nine confirmed false-negative studies. These studies included four cases of slightly enlarged heart size, four cases of slightly increased pulmonary opacification and one case with a small unilateral pleural effusion. This gives the AI a sensitivity of 99.8% (95% CI= 99.65-99.92) and specificity of 36.4 % (95% CI= 35.05-37.84) for recognising significant pathology on a chest X-ray. Conclusions: AI was able to correctly rule out 36.4% of chest X-rays with no significant findings of primary health care patients, with a minimal number of false negatives that would lead to effectively no compromise on patient safety. No critical findings were missed by the software., Comment: Abstract #21318 - ECR 2022 oral presentations

Published
2022

2. Critical Evaluation of Deep Neural Networks for Wrist Fracture Detection

Author

Raisuddin, Abu Mohammed, Vaattovaara, Elias, Nevalainen, Mika, Nikki, Marko, Järvenpää, Elina, Makkonen, Kaisa, Pinola, Pekka, Palsio, Tuula, Niemensivu, Arttu, Tervonen, Osmo, and Tiulpin, Aleksei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Quantitative Biology - Quantitative Methods
Abstract

Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection -- DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set -- average precision of 0.99 (0.99-0.99) vs 0.64 (0.46-0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98-0.99) vs 0.84 (0.72-0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.

Published
2020

3. Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

Author

Solovyev, Roman, Melekhov, Iaroslav, Lesonen, Timo, Vaattovaara, Elias, Tervonen, Osmo, and Tiulpin, Aleksei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Cardiothoratic ratio (CTR) estimated from chest radiographs is a marker indicative of cardiomegaly, the presence of which is in the criteria for heart failure diagnosis. Existing methods for automatic assessment of CTR are driven by Deep Learning-based segmentation. However, these techniques produce only point estimates of CTR but clinical decision making typically assumes the uncertainty. In this paper, we propose a novel method for chest X-ray segmentation and CTR assessment in an automatic manner. In contrast to the previous art, we, for the first time, propose to estimate CTR with uncertainty bounds. Our method is based on Deep Convolutional Neural Network with Feature Pyramid Network (FPN) decoder. We propose two modifications of FPN: replace the batch normalization with instance normalization and inject the dropout which allows to obtain the Monte-Carlo estimates of the segmentation maps at test time. Finally, using the predicted segmentation mask samples, we estimate CTR with uncertainty. In our experiments we demonstrate that the proposed method generalizes well to three different test sets. Finally, we make the annotations produced by two radiologists for all our datasets publicly available., Comment: Roman Solovyev and Iaroslav Melekhov contributed equally. Timo Lesonen and Elias Vaattovaara contributed equally

Published
2019

4. The Effect of Zoledronic Acid on Serum Biomarkers among Patients with Chronic Low Back Pain and Modic Changes in Lumbar Magnetic Resonance Imaging.

Author

Koivisto, Katri, Karppinen, Jaro, Haapea, Marianne, Järvinen, Jyri, Kyllönen, Eero, Tervonen, Osmo, Niinimäki, Jaakko, Alini, Mauro, Lotz, Jeffrey, Dudli, Stefan, Samartzis, Dino, Risteli, Juha, Majuri, Marja-Leena, Alenius, Harri, and Grad, Sibylle

Subjects
Modic change, chronic low back pain, magnetic resonance imaging, randomized trial, serum biomarkers, zoledronic acid
Abstract

The aim of the current study was to compare changes in serum biomarkers, including inflammatory mediators, signaling molecules, growth factors and markers of bone turnover after a single intravenous infusion of 5 mg zoledronic acid (ZA, a long-acting bisphosphonate; n = 20) or placebo (n = 20) among patients with Modic changes (MC) and chronic low back pain in a randomized controlled design. The MCs were classified into M1, predominating M1, predominating M2, and M2. We measured the serum concentrations of 39 biomarkers at baseline, and one month and one year after treatment. After Benjamini-Hochberg (B-H) correction, we observed significant differences in three biomarkers over one year: Interferon-γ-inducible protein (IP10) had risen in the ZA group (p = 0.005), whereas alkaline phosphatase (AFOS) and intact procollagen I N-terminal propeptide (iPINP) had significantly decreased in the ZA group, but had not changed in the placebo group (p < 0.001 for both). Change in iPINP correlated with change in the volume of all MC and M1 lesions. ZA downregulated bone turnover markers as expected and, surprisingly, increased the chemokine IP-10 relative to placebo treatment. This adds to our knowledge of the effects of ZA on MC and the biomarkers that signal this process.

Published
2019

6. Bayesian Feature Pyramid Networks for Automatic Multi-label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio

Author

Solovyev, Roman, Melekhov, Iaroslav, Lesonen, Timo, Vaattovaara, Elias, Tervonen, Osmo, Tiulpin, Aleksei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Blanc-Talon, Jacques, editor, Delmas, Patrice, editor, Philips, Wilfried, editor, Popescu, Dan, editor, and Scheunders, Paul, editor

Published
2020
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14. An Allele of COL9A2 Associated with Intervertebral Disc Disease

Author

Annunen, Susanna, Paassilta, Petteri, Lohiniva, Jaana, Perälä, Merja, Pihlajamaa, Tero, Karppinen, Jaro, Tervonen, Osmo, Kröger, Heikki, Lähde, Seppo, Vanharanta, Heikki, Ryhänen, Lasse, Ott, Jürg, Prockop, Darwin J., and Ala-Kokko, Leena

Published
1999

36. Valence Scaling of Dynamic Facial Expressions is Altered in High-Functioning Subjects with Autism Spectrum Disorders: an fMRI Study

Author

Rahko, Jukka S., Paakki, Jyri-Johan, Starck, Tuomo H., Nikkinen, Juha, Pauls, David L., Kätsyri, Jari V., Jansson-Verkasalo, Eira M., Carter, Alice S., Hurtig, Tuula M., Mattila, Marja-Leena, Jussila, Katja K., Remes, Jukka J., Kuusikko-Gauffin, Sanna A., Sams, Mikko E., Bölte, Sven, Ebeling, Hanna E., Moilanen, Irma K., Tervonen, Osmo, and Kiviniemi, Vesa

Published
2012
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44. Co‐activation pattern alterations in autism spectrum disorder–A volume‐wise hierarchical clustering fMRI study

Author

Paakki, Jyri‐Johan, primary, Rahko, Jukka S., additional, Kotila, Aija, additional, Mattila, Marja‐Leena, additional, Miettunen, Helena, additional, Hurtig, Tuula M., additional, Jussila, Katja K., additional, Kuusikko‐Gauffin, Sanna, additional, Moilanen, Irma K., additional, Tervonen, Osmo, additional, and Kiviniemi, Vesa J., additional

Published
2021
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