| 1. |
- Cai, Zongye, et al.
(author)
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Kynurenine metabolites predict survival in pulmonary arterial hypertension : A role for IL-6/IL-6Rα
- 2022
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In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 12
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Journal article (peer-reviewed)abstract
- Activation of the kynurenine pathway (KP) has been reported in patients with pulmonary arterial hypertension (PAH) undergoing PAH therapy. We aimed to determine KP-metabolism in treatment-naïve PAH patients, investigate its prognostic values, evaluate the effect of PAH therapy on KP-metabolites and identify cytokines responsible for altered KP-metabolism. KP-metabolite levels were determined in plasma from PAH patients (median follow-up 42 months) and in rats with monocrotaline- and Sugen/hypoxia-induced PH. Blood sampling of PAH patients was performed at the time of diagnosis, six months and one year after PAH therapy. KP activation with lower tryptophan, higher kynurenine (Kyn), 3-hydroxykynurenine (3-HK), quinolinic acid (QA), kynurenic acid (KA), and anthranilic acid was observed in treatment-naïve PAH patients compared with controls. A similar KP-metabolite profile was observed in monocrotaline, but not Sugen/hypoxia-induced PAH. Human lung primary cells (microvascular endothelial cells, pulmonary artery smooth muscle cells, and fibroblasts) were exposed to different cytokines in vitro. Following exposure to interleukin-6 (IL-6)/IL-6 receptor α (IL-6Rα) complex, all cell types exhibit a similar KP-metabolite profile as observed in PAH patients. PAH therapy partially normalized this profile in survivors after one year. Increased KP-metabolites correlated with higher pulmonary vascular resistance, shorter six-minute walking distance, and worse functional class. High levels of Kyn, 3-HK, QA, and KA measured at the latest time-point were associated with worse long-term survival. KP-metabolism was activated in treatment-naïve PAH patients, likely mediated through IL-6/IL-6Rα signaling. KP-metabolites predict response to PAH therapy and survival of PAH patients.
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| 2. |
- Oettl, Felix C., et al.
(author)
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A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
- 2024
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In: JOURNAL OF EXPERIMENTAL ORTHOPAEDICS. - 2197-1153. ; 11:3
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Research review (peer-reviewed)abstract
- Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.
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| 3. |
- Oettl, Felix C., et al.
(author)
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A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?
- 2024
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In: Journal of Experimental Orthopaedics. - 2197-1153. ; 11:3
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Research review (peer-reviewed)abstract
- Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.
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| 4. |
- Oettl, Felix C., et al.
(author)
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The artificial intelligence advantage: Supercharging exploratory data analysis
- 2024
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In: Knee Surgery, Sports Traumatology, Arthroscopy. - 1433-7347 .- 0942-2056. ; In Press
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Research review (peer-reviewed)abstract
- Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.
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| 5. |
- Zsidai, Balint, 1993, et al.
(author)
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A practical guide to the implementation of AI in orthopaedic research – part 1: opportunities in clinical application and overcoming existing challenges
- 2023
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In: Journal of Experimental Orthopaedics. - 2197-1153. ; 10:1
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Research review (peer-reviewed)abstract
- Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV
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| 6. |
- Zsidai, Bálint, et al.
(author)
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A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction
- 2024
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In: Journal of Experimental Orthopaedics. - 2197-1153. ; 11:3
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Research review (peer-reviewed)abstract
- Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI-based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self-supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI-driven orthopaedic research. Level of Evidence: Level IV.
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| 7. |
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| 8. |
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- 2019
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Journal article (peer-reviewed)
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