scholarly journals A deep learning method for predicting knee osteoarthritis radiographic progression from MRI

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Jean-Baptiste Schiratti ◽  
Rémy Dubois ◽  
Paul Herent ◽  
David Cahané ◽  
Jocelyn Dachary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Osteoarthritis ◽  
Joint Space ◽  
Cartilage Degradation ◽  
Classification Model ◽  
Womac Pain ◽  
Learning Method ◽  
Mr Images ◽  
Disease Modifying Drugs ◽  
Pain Prediction

Abstract Background The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. Methods Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. Results Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction. Conclusions This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.

scholarly journals A Deep Learning Method for Identifying Predictors of Knee Osteoarthritis Radiographic Progression From Baseline MRI

2021 ◽  
Author(s):  
Jean-Baptiste Schiratti ◽  
Rémy Dubois ◽  
Paul Herent ◽  
David Cahané ◽  
Jocelyn Dachary ◽  
...  
Keyword(s):  
Deep Learning ◽  
Knee Osteoarthritis ◽  
Joint Space ◽  
Cartilage Degradation ◽  
Magnetic Resonance Images ◽  
Classification Model ◽  
Womac Pain ◽  
Learning Method ◽  
Disease Modifying Drugs ◽  
Using Data

Abstract -- Background --The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. -- Methods --Using data from the Osteoarthritis Initiative database (OAI), we implemented a Deep Learning method to predict, from baseline magnetic resonance images, further cartilage degradation, the latter being measured by Joint Space Narrowing at 12 months. -- Results --Using COR IW TSE images, our classification model achieved a ROC AUC score of 65% to be compared with a ROC AUC score of 58.7% obtained by trained radiologists. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the internal femoro-tibial compartment for JSN progression and the intra-articular space for pain prediction. -- Conclusions --This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.

scholarly journals EMBER: Multi-label prediction of kinase-substrate phosphorylation events through deep learning

2020 ◽  
Author(s):  
Kathryn E. Kirchoff ◽  
Shawn M. Gomez
Keyword(s):  
Deep Learning ◽  
Experimental Studies ◽  
Classification Model ◽  
Learning Method ◽  
Kinase Substrate ◽  
Phosphorylation Of Proteins ◽  
Separate Model ◽  
Label Prediction ◽  
Substrate Phosphorylation ◽  
Vector Representations

AbstractKinase-catalyzed phosphorylation of proteins forms the backbone of signal transduction within the cell, enabling the coordination of numerous processes such as the cell cycle, apoptosis, and differentiation. While on the order of 105 phosphorylation events have been described, we know the specific kinase performing these functions for less than 5% of cases. The ability to predict which kinases initiate specific individual phosphorylation events has the potential to greatly enhance the design of downstream experimental studies, while simultaneously creating a preliminary map of the broader phosphorylation network that controls cellular signaling. To this end, we describe EMBER, a deep learning method that integrates kinase-phylogeny information and motif-dissimilarity information into a multi-label classification model for the prediction of kinase-motif phosphorylation events. Unlike previous deep learning methods that perform single-label classification, we restate the task of kinase-motif phosphorylation prediction as a multi-label problem, allowing us to train a single unified model rather than a separate model for each of the 134 kinase families. We utilize a Siamese network to generate novel vector representations, or an embedding, of motif sequences, and we compare our novel embedding to a previously proposed peptide embedding. Our motif vector representations are used, along with one-hot encoded motif sequences, as input to a classification network while also leveraging kinase phylogenetic relationships into our model via a kinase phylogeny-based loss function. Results suggest that this approach holds significant promise for improving our map of phosphorylation relations that underlie kinome signaling.Availabilityhttps://github.com/gomezlab/EMBER

Deep learning method for segmentation of rotator cuff muscles on MR images

2020 ◽  
Author(s):  
Giovanna Medina ◽  
Colleen G. Buckless ◽  
Eamon Thomasson ◽  
Luke S. Oh ◽  
Martin Torriani
Keyword(s):  
Deep Learning ◽  
Rotator Cuff ◽  
Learning Method ◽  
Mr Images

scholarly journals Think Twice: A Post-Processing Approach for the Chinese Spelling Error Correction

2021 ◽  
Vol 11 (13) ◽  
pp. 5832
Author(s):  
Wei Gou ◽  
Zheng Chen
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Error Correction ◽  
Language Processing ◽  
Classification Model ◽  
Spelling Error ◽  
Learning Method ◽  
Post Processing ◽  
Rule Based ◽  
Model Training

Chinese Spelling Error Correction is a hot subject in the field of natural language processing. Researchers have already produced many great solutions, from the initial rule-based solution to the current deep learning method. At present, SpellGCN, proposed by Alibaba’s team, achieves the best results of which character level precision over SIGHAN2013 is 98.4%. However, when we apply this algorithm to practical error correction tasks, it produces many false error correction results. We believe that this is because the corpus used for model training contains significantly more errors than the text used for model correcting. In response to this problem, we propose performing a post-processing operation on the error correction tasks. We employ the initial model’s output as a candidate character, obtain various features of the character itself and its context, and then use a classification model to filter the initial model’s false error correction results. The post-processing idea introduced in this paper can apply to most Chinese Spelling Error Correction models to improve their performance over practical error correction tasks.

scholarly journals Fresh autologous stromal tissue fraction for the treatment of knee osteoarthritis related pain and disability

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Stefano Santoprete ◽  
Federica Marchetti ◽  
Carlotta Rubino ◽  
Maria Grazie Bedini ◽  
Luigi Aurelio Nasto ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Adult Population ◽  
Stromal Vascular Fraction ◽  
Case Series ◽  
Cartilage Degradation ◽  
Womac Pain ◽  
Score Change ◽  
Anabolic Action ◽  
Stromal Tissue ◽  
Tissue Fraction

Knee osteoarthritis (KOA) is a very common condition with multifactorial etiology leading to severe pain and disability in the adult population. Although KOA is considered a non-inflammatory arthritis, upregulation of inflammatory and catabolic pathways with increased production of proinflammatory cytokines leading to cartilage degradation and extracellular matrix degeneration has been reported. Intra-articular injection of fresh fat derived stromal vascular fraction (SVF) fraction has been proposed as a valid and alternative treatment for symptomatic KOA that guarantees mechanical support through viscosupplementation, anti-inflammatory, and anabolic action. We retrospectively reviewed a case series of 84 consecutive adult patients with KOA who underwent intra-articular injection of fresh fat derived SVF. Significant improvement in pain levels (NRS score decrease 3.5±1.1, p<0.001), WOMAC pain (-7.02±3.45 score change, p<0.001), WOMAC stiffness (-1.97±1.02, p<0.001), and ROM improvement (+17.13±5.22°, p<0.001). The only complication noted was knee joint swelling lasting for less than 7 days after the injection in 7% of the patients.

scholarly journals Long-term structural and symptomatic effects of intra-articular sprifermin in patients with knee osteoarthritis: 5-year results from the FORWARD study

2021 ◽  
pp. annrheumdis-2020-219181
Author(s):  
Felix Eckstein ◽  
Marc C Hochberg ◽  
Hans Guehring ◽  
Flavie Moreau ◽  
Victor Ona ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Joint Space Width ◽  
Joint Space ◽  
Trend Test ◽  
Womac Pain ◽  
Efficacy And Safety ◽  
Primary Analysis ◽  
Registration Number

ObjectiveThe FORWARD (FGF-18 Osteoarthritis Randomized Trial with Administration of Repeated Doses) trial assessed efficacy and safety of the potential disease-modifying osteoarthritis drug (DMOAD) sprifermin in patients with knee osteoarthritis. Here, we report 5-year efficacy and safety results.MethodsPatients were randomised to intra-articular sprifermin 100 µg or 30 µg every 6 months (q6mo) or 12 months, or placebo, for 18 months. The primary analysis was at year 2, with follow-up at years 3, 4 and 5. Additional post hoc exploratory analyses were conducted in patients with baseline minimum radiographic joint space width 1.5–3.5 mm and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain 40–90, a subgroup at risk (SAR) of progression.Results378 (69%) patients completed the 5-year follow-up. A significant dose-response in total femorotibial joint cartilage thickness with sprifermin (trend test, p<0.001) and a 0.05 mm mean difference with sprifermin 100 µg q6mo versus placebo (95% CI 0.00 to 0.10; p=0.015) were sustained to year 5. WOMAC pain scores improved ~50% from baseline in all groups. No patient in the 100 µg q6mo group had replacement of the treated knee. 96%–98% of patients receiving sprifermin and 98% placebo reported adverse events, most were mild or moderate and deemed unrelated to treatment. Adverse event-related study withdrawals were <10%. Differentiation in WOMAC pain between sprifermin 100 µg q6mo and placebo in the SAR (n=161) at year 3 was maintained to year 5 (−10.08; 95% CI −25.68 to 5.53).ConclusionIn the longest DMOAD trial reported to date, sprifermin maintained long-term structural modification of articular cartilage over 3.5 years post-treatment. Potential translation to clinical benefit was observed in the SAR.Trial registration numberNCT01919164

scholarly journals Molecular Classification of Knee Osteoarthritis

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhongyang Lv ◽  
Yannick Xiaofan Yang ◽  
Jiawei Li ◽  
Yuxiang Fei ◽  
Hu Guo ◽  
...  
Keyword(s):  
Knee Osteoarthritis ◽  
Molecular Classification ◽  
Cartilage Degradation ◽  
Clinical Findings ◽  
Molecular Characteristics ◽  
Disease Modifying Drugs ◽  
Therapeutic Mechanisms ◽  
End Stage ◽  
Molecular Pathophysiology

Knee osteoarthritis (KOA) is the most common form of joint degeneration with increasing prevalence and incidence in recent decades. KOA is a molecular disorder characterized by the interplay of numerous molecules, a considerable number of which can be detected in body fluids, including synovial fluid, urine, and blood. However, the current diagnosis and treatment of KOA mainly rely on clinical and imaging manifestations, neglecting its molecular pathophysiology. The mismatch between participants’ molecular characteristics and drug therapeutic mechanisms might explain the failure of some disease-modifying drugs in clinical trials. Hence, according to the temporal alteration of representative molecules, we propose a novel molecular classification of KOA divided into pre-KOA, early KOA, progressive KOA, and end-stage KOA. Then, progressive KOA is furtherly divided into four subtypes as cartilage degradation-driven, bone remodeling-driven, inflammation-driven, and pain-driven subtype, based on the major pathophysiology in patient clusters. Multiple clinical findings of representatively investigated molecules in recent years will be reviewed and categorized. This molecular classification allows for the prediction of high-risk KOA individuals, the diagnosis of early KOA patients, the assessment of therapeutic efficacy, and in particular, the selection of homogenous patients who may benefit most from the appropriate therapeutic agents.

scholarly journals Subchondral bone length in knee osteoarthritis: A deep learning derived imaging measure and its association with radiographic and clinical outcomes

2021 ◽  
Author(s):  
Gary H Chang ◽  
Lisa K Park ◽  
Nina A Le ◽  
Ray S Jhun ◽  
Tejus Surendran ◽  
...  
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Subchondral Bone ◽  
Joint Space ◽  
Knee Joints ◽  
Womac Pain ◽  
Odds Ratios ◽  
Bone Length ◽  
Osteoarthritis Initiative ◽  
Mri Sequences

Objective: Develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity. Methods: A fast region-based convolutional neural network was trained to crop the knee joints in sagittal dual-echo steady state MRI sequences obtained from the Osteoarthritis Initiative (OAI). Publicly available annotations of the cartilage and menisci were used as references to annotate the tibia and the femur in 61 knees. Another deep neural network (U-Net) was developed to learn these annotations. Model predictions were compared with radiologist-driven annotations on an independent test set (27 knees). The U-Net was applied to automatically extract the knee joint structures on the larger OAI dataset (9,434 knees). We defined subchondral bone length (SBL), a novel shape measure characterizing the extent of overlying cartilage and bone flattening, and examined its relationship with radiographic joint space narrowing (JSN), concurrent WOMAC pain and disability as well as subsequent partial or total knee replacement (KR). Odds ratios for each outcome were estimated using relative changes in SBL on the OAI dataset into quartiles. Result: Mean SBL values for knees with JSN were consistently different from knees without JSN. Greater changes of SBL from baseline were associated with greater pain and disability. For knees with medial or lateral JSN, the odds ratios between lowest and highest quartiles corresponding to SBL changes for future KR were 5.68 (95% CI:[3.90,8.27]) and 7.19 (95% CI:[3.71,13.95]), respectively. Conclusion: SBL quantified OA status based on JSN severity. It has promise as an imaging marker in predicting clinical and structural OA outcomes.

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