Skip to main content
Hauptrubriken
CME Facharzt-Training Webinare Zeitschriften Bücher e.Medpedia Podcast
Service
Jobbörse Info & Hilfe
Fachgebiete
Anästhesiologie Allgemeinmedizin Arbeitsmedizin Augenheilkunde Chirurgie Dermatologie Gynäkologie und Geburtshilfe HNO Innere Medizin Kardiologie Neurologie Onkologie und Hämatologie Orthopädie und Unfallchirurgie Pädiatrie Pathologie Psychiatrie Radiologie Rechtsmedizin Urologie Zahnmedizin
Themen
Klimawandel und Gesundheit Neues aus dem Markt COVID-19 Praxis und Beruf Seltene Erkrankungen GOÄ & EBM Panorama
Sammlungen
Kasuistiken Algorithmen & Infografiken Blickdiagnosen Arthropedia FlapFinder (für Dermatochirurgen) Videos Kongressberichterstattung Medizin für Apothekerinnen und Apotheker Für Ärztinnen und Ärzte in Weiterbildung Für Medizinstudierende
Erweiterte Suche
Anmelden
nach oben
Skeletal Radiology
Erschienen in: Skeletal Radiology 2/2022

04.11.2021 | Review Article

AI MSK clinical applications: cartilage and osteoarthritis

verfasst von: Gabby B. Joseph, Charles E. McCulloch, Jae Ho Sohn, Valentina Pedoia, Sharmila Majumdar, Thomas M. Link

Erschienen in: Skeletal Radiology | Ausgabe 2/2022

Einloggen, um Zugang zu erhalten

Abstract

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.
Literatur
1.
Murray CJ, Vos T, Lozano R, Naghavi M, Flaxman AD, Michaud C, et al. Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2197–223.PubMedCrossRef
2.
Murphy L, Helmick CG. The impact of osteoarthritis in the United States: a population-health perspective. Am J Nurs. 2012;112(3 Suppl 1):S13–9.PubMedCrossRef
3.
Weinstein AM, Rome BN, Reichmann WM, Collins JE, Burbine SA, Thornhill TS, et al. Estimating the burden of total knee replacement in the United States. J Bone Joint Surg Am. 2013;95(5):385–92.PubMedPubMedCentralCrossRef
4.
Center ME-bP. Total knee replacement. Minneapolis: Agency for Healthcare and Research Quality; 2003.
5.
6.
Dell’Isola A, Allan R, Smith SL, Marreiros SS, Steultjens M. Identification of clinical phenotypes in knee osteoarthritis: a systematic review of the literature. BMC Musculoskelet Disord. 2016;17(1):425.PubMedPubMedCentralCrossRef
7.
Waarsing JH, Bierma-Zeinstra SM, Weinans H. Distinct subtypes of knee osteoarthritis: data from the Osteoarthritis Initiative. Rheumatology (Oxford). 2015;54(9):1650–8.CrossRef
8.
Deveza LA, Melo L, Yamato TP, Mills K, Ravi V, Hunter DJ. Knee osteoarthritis phenotypes and their relevance for outcomes: a systematic review. Osteoarthritis Cartilage. 2017;25(12):1926–41.PubMedCrossRef
9.
Roddy E, Doherty M. Changing life-styles and osteoarthritis: what is the evidence? Best Pract Res Clin Rheumatol. 2006;20(1):81–97.PubMedCrossRef
10.
Cooper C, Snow S, McAlindon TE, Kellingray S, Stuart B, Coggon D, et al. Risk factors for the incidence and progression of radiographic knee osteoarthritis. Arthritis Rheum. 2000;43(5):995–1000.PubMedCrossRef
11.
Musumeci G, Aiello FC, Szychlinska MA, Di Rosa M, Castrogiovanni P, Mobasheri A. Osteoarthritis in the XXIst century: risk factors and behaviours that influence disease onset and progression. Int J Mol Sci. 2015;16(3):6093–112.PubMedPubMedCentralCrossRef
12.
Sharma L, Nevitt M, Hochberg M, Guermazi A, Roemer FW, Crema M, et al. Clinical significance of worsening versus stable preradiographic MRI lesions in a cohort study of persons at higher risk for knee osteoarthritis. Ann Rheum Dis. 2016;75(9):1630–6.PubMedCrossRef
13.
Baum T, Joseph GB, Arulanandan A, Nardo L, Virayavanich W, Carballido-Gamio J, et al. Association of magnetic resonance imaging-based knee cartilage T2 measurements and focal knee lesions with knee pain: data from the Osteoarthritis Initiative. Arthritis Care Res. 2012;64(2):248–55.CrossRef
14.
Joseph GB, Baum T, Alizai H, Carballido-Gamio J, Nardo L, Virayavanich W, et al. Baseline mean and heterogeneity of MR cartilage T2 are associated with morphologic degeneration of cartilage, meniscus, and bone marrow over 3 years–data from the Osteoarthritis Initiative. Osteoarthr Cartil. 2012;20(7):727–35.CrossRef
15.
16.
Kokkotis C, Moustakidis S, Papageorgiou E, Giakas G, Tsaopoulos DE. Machine learning in knee osteoarthritis: a review. Osteoarthritis and Cartilage Open. 2020;2(3):100069.
17.
Hirschmann A, Cyriac J, Stieltjes B, Kober T, Richiardi J, Omoumi P. Artificial intelligence in musculoskeletal imaging: review of current literature, challenges, and trends. Semin Musculoskelet Radiol. 2019;23(3):304–11.PubMedCrossRef
18.
Joseph GB, McCulloch CE, Nevitt MC, Neumann J, Gersing AS, Kretzschmar M, et al. Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI: data from the osteoarthritis initiative. J Magn Reson Imaging. 2018;47(6):1517–26.PubMedCrossRef
19.
Norman B, Pedoia V, Noworolski A, Link TM, Majumdar S. Applying densely connected convolutional neural networks for staging osteoarthritis severity from plain radiographs. J Digit Imaging. 2019;32(3):471–7.PubMedCrossRef
20.
Furche T, Gottlob G, Libkin L, Orsi G, Paton NW. Data wrangling for big data: challenges and opportunities. InEDBT. 2016;16:473–8.
21.
22.
Jerez JM, Molina I, García-Laencina PJ, Alba E, Ribelles N, Martín M, et al. Missing data imputation using statistical and machine learning methods in a real breast cancer problem. Artif Intell Med. 2010;50(2):105–15.PubMedCrossRef
23.
Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS, Srivastava G, et al. Analysis of dimensionality reduction techniques on big data. IEEE Access. 2020;8:54776–88.CrossRef
24.
Jamshidi A, Pelletier JP, Martel-Pelletier J. Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat Rev Rheumatol. 2019;15(1):49–60.PubMedCrossRef
25.
Guo X, Yin Y, Dong C, Yang G, Zhou G. On the class imbalance problem. In: 2008 Fourth international conference on natural computation. IEEE; 2008. Vol. 4, pp. 192–201.
26.
Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63.PubMedCrossRef
27.
Hugle M, Omoumi P, van Laar JM, Boedecker J, Hugle T. Applied machine learning and artificial intelligence in rheumatology. Rheumatol Adv Pract. 2020;4(1):rkaa005.PubMedPubMedCentralCrossRef
28.
Webb GI, Keogh E, Miikkulainen R. Naïve Bayes. Encyclopedia of machine learning. 2010;15:713-4.
29.
Singh A, Thakur N, Sharma A, editors. A review of supervised machine learning algorithms. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom); 2016 16–18 March 2016.
30.
Kherif F, Latypova A. Chapter 12 - Principal component analysis. In: Mechelli A, Vieira S, editors. Machine Learning. Academic Press; 2020. p. 209–25.CrossRef
31.
Kokkotis C, Moustakidis S, Papageorgiou E, Giakas G, Tsaopoulos DE. Machine learning in knee osteoarthritis: a review. Osteoarthr Cartil Open. 2020;2(3):100069.CrossRef
32.
33.
34.
Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011;4(1):299.PubMedPubMedCentralCrossRef
35.
Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:201016061. 2020.
36.
37.
Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.CrossRef
38.
Laber EB, Murphy SA, editors. Small sample inference for generalization error in classification using the CUD bound. Uncertainty in artificial intelligence: proceedings of the conference Conference on Uncertainty in Artificial Intelligence; 2008: NIH Public Access.
39.
Stead WW. Clinical implications and challenges of artificial intelligence and deep learning. JAMA. 2018;320(11):1107–8.PubMedCrossRef
40.
Kerkhof HJ, Bierma-Zeinstra SM, Arden NK, Metrustry S, Castano-Betancourt M, Hart DJ, et al. Prediction model for knee osteoarthritis incidence, including clinical, genetic and biochemical risk factors. Ann Rheum Dis. 2014;73(12):2116–21.PubMedCrossRef
41.
Ashinsky BG, Bouhrara M, Coletta CE, Lehallier B, Urish KL, Lin PC, et al. Predicting early symptomatic osteoarthritis in the human knee using machine learning classification of magnetic resonance images from the osteoarthritis initiative. J Orthop Res. 2017;35(10):2243–50.PubMedPubMedCentralCrossRef
42.
Štajduhar I, Mamula M, Miletić D, Ünal G. Semi-automated detection of anterior cruciate ligament injury from MRI. Comput Methods Programs Biomed. 2017;140:151–64.PubMedCrossRef
43.
Lazzarini N, Runhaar J, Bay-Jensen AC, Thudium CS, Bierma-Zeinstra SMA, Henrotin Y, et al. A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthr Cartil. 2017;25(12):2014–21.CrossRef
44.
Halilaj E, Le Y, Hicks JL, Hastie TJ, Delp SL. Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthr Cartil. 2018;26(12):1643–50.CrossRef
45.
Liu F, Zhou Z, Samsonov A, Blankenbaker D, Larison W, Kanarek A, et al. Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology. 2018;289(1):160–9.PubMedCrossRef
46.
Tiulpin A, Thevenot J, Rahtu E, Lehenkari P, Saarakkala S. Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep. 2018;8(1):1727.PubMedPubMedCentralCrossRef
47.
Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med. 2018;15(11):e1002699.PubMedPubMedCentralCrossRef
48.
Du Y, Almajalid R, Shan J, Zhang M. A novel method to predict knee osteoarthritis progression on MRI using machine learning methods. IEEE Trans Nanobioscience. 2018;17(3):228–36.PubMedCrossRef
49.
Antony J. Automatic quantification of radiographic knee osteoarthritis severity and associated diagnostic features using deep convolutional neural networks (Doctoral dissertation, Dublin City University). 2018. http://​doras.​dcu.​ie/​22154/​.
50.
Pedoia V, Norman B, Mehany SN, Bucknor MD, Link TM, Majumdar S. 3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects. J Magn Reson Imaging. 2019;49(2):400–10.PubMedCrossRef
51.
Nelson AE, Fang F, Arbeeva L, Cleveland RJ, Schwartz TA, Callahan LF, et al. A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium. Osteoarthr Cartil. 2019;27(7):994–1001.CrossRef
52.
Brahim A, Jennane R, Riad R, Janvier T, Khedher L, Toumi H, et al. A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: data from the OsteoArthritis Initiative. Comput Med Imaging Graph. 2019;73:11–8.PubMedCrossRef
53.
Roblot V, Giret Y, Bou Antoun M, Morillot C, Chassin X, Cotten A, et al. Artificial intelligence to diagnose meniscus tears on MRI. Diagn Interv Imaging. 2019;100(4):243–9.PubMedCrossRef
54.
Couteaux V, Si-Mohamed S, Nempont O, Lefevre T, Popoff A, Pizaine G, et al. Automatic knee meniscus tear detection and orientation classification with Mask-RCNN. Diagn Interv Imaging. 2019;100(4):235–42.PubMedCrossRef
55.
Abedin J, Antony J, McGuinness K, Moran K, O’Connor NE, Rebholz-Schuhmann D, et al. Predicting knee osteoarthritis severity: comparative modeling based on patient’s data and plain X-ray images. Sci Rep. 2019;9(1):5761.PubMedPubMedCentralCrossRef
56.
57.
Pedoia V, Lee J, Norman B, Link TM, Majumdar S. Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort. Osteoarthritis Cartilage. 2019;27(7):1002–10.PubMedPubMedCentralCrossRef
58.
Tiulpin A, Klein S, Bierma-Zeinstra SMA, Thevenot J, Rahtu E, Meurs JV, et al. Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci Rep. 2019;9(1):20038.PubMedPubMedCentralCrossRef
59.
Jamshidi A, Leclercq M, Labbe A, Pelletier JP, Abram F, Droit A, et al. Identification of the most important features of knee osteoarthritis structural progressors using machine learning methods. Ther Adv Musculoskelet Dis. 2020;12:1759720X20933468.PubMedPubMedCentralCrossRef
60.
Leung K, Zhang B, Tan J, Shen Y, Geras KJ, Babb JS, et al. Prediction of total knee replacement and diagnosis of osteoarthritis by using deep learning on knee radiographs: data from the Osteoarthritis Initiative. Radiology. 2020;296(3):584–93.PubMedCrossRef
61.
Widera P, Welsing PMJ, Ladel C, Loughlin J, Lafeber F, Petit Dop F, et al. Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Sci Rep. 2020;10(1):8427.PubMedPubMedCentralCrossRef
62.
Alexos A, Moustakidis S, Kokkotis C, Tsaopoulos D. Physical activity as a risk factor in the progression of osteoarthritis: a machine learning perspective. In: International Conference on Learning and Intelligent Optimization. Cham: Springer; 2020. pp. 16–26.
63.
Thomas KA, Kidziński Ł, Halilaj E, Fleming SL, Venkataraman GR, Oei EH, et al. Automated classification of radiographic knee osteoarthritis severity using deep neural networks. Radiol Artif Intell. 2020;2(2):e190065.PubMedPubMedCentralCrossRef
64.
Kwon SB, Ku Y, Han HU, Lee MC, Kim HC, Ro DH. A machine learning-based diagnostic model associated with knee osteoarthritis severity. Sci Rep. 2020;10(1):15743.PubMedPubMedCentralCrossRef
65.
Guan B, Liu F, Haj-Mirzaian A, Demehri S, Samsonov A, Neogi T, et al. Deep learning risk assessment models for predicting progression of radiographic medial joint space loss over a 48-MONTH follow-up period. Osteoarthr Cartil. 2020;28(4):428–37.CrossRef
66.
Guan B, Liu F, Matthew P, Mirzaian AH, Demehri S, Neogi T, et al. Deep learning approach to predict pain progression in knee osteoarthritis. Osteoarthr Cartil. 2020;28:S316.CrossRef
67.
Tolpadi AA, Lee JJ, Pedoia V, Majumdar S. Deep learning predicts total knee replacement from magnetic resonance images. Sci Rep. 2020;10(1):6371.PubMedPubMedCentralCrossRef
68.
Razmjoo A, Caliva F, Lee J, Liu F, Joseph GB, Link TM, et al. T2 analysis of the entire osteoarthritis initiative dataset. J Orthop Res. 2021;39(1):74–85.PubMedCrossRef
69.
Joseph GB, Mcculloch CE, Nevitt MC, Link TM, JH. S, editors. Machine learning for predicting knee osteoarthritis progression over 8 years using combined MR imaging features, demographics, and clinical factors: data from the Osteoarthritis Initiative World Congress on Osteoarthritis; 2021; Virtual due to Covid 19.
70.
Gan H-S, Ramlee MH, Wahab AA, Lee Y-S, Shimizu A. From classical to deep learning: review on cartilage and bone segmentation techniques in knee osteoarthritis research. Artif Intell Rev. 2021;54(4):2445–94.CrossRef
71.
72.
Xia Y. Magic-angle effect in magnetic resonance imaging of articular cartilage: a review. Invest Radiol. 2000;35(10):602–21.PubMedCrossRef
73.
Joseph GB, McCulloch CE, Nevitt MC, Gersing AS, Schwaiger BJ, Kretzschmar M, et al. Medial femur T2 Z-scores predict the probability of knee structural worsening over 4–8 years: data from the osteoarthritis initiative. J Magn Reson Imaging. 2017;46(4):1128–36.PubMedPubMedCentralCrossRef
74.
Li X, Ma C, Link T, Castillo D, Blumenkrantz G, Lozano J, et al. In vivo T1rho and T2 mapping of articular cartilage in osteoarthritis of the knee using 3 Tesla MRI. Osteoarthr Cartil. 2007;15(7):789–97.CrossRef
75.
Stehling C, Liebl H, Krug R, Lane NE, Nevitt MC, Lynch J, et al. Patellar cartilage: T2 values and morphologic abnormalities at 3.0-T MR imaging in relation to physical activity in asymptomatic subjects from the osteoarthritis initiative. Radiology. 2010;254(2):509–20.PubMedPubMedCentralCrossRef
76.
Mosher TJ, Smith HE, Collins C, Liu Y, Hancy J, Dardzinski BJ, et al. Change in knee cartilage T2 at MR imaging after running: a feasibility study. Radiology. 2005;234(1):245–9.PubMedCrossRef
77.
Serebrakian AT, Poulos T, Liebl H, Joseph GB, Lai A, Nevitt MC, et al. Weight loss over 48 months is associated with reduced progression of cartilage T2 relaxation time values: data from the osteoarthritis initiative. J Magn Reson Imaging. 2015;41(5):1272–80.PubMedCrossRef
78.
Ferrero G, Sconfienza LM, Fiz F, Fabbro E, Corazza A, Dettore D, et al. Effect of intra-articular injection of intermediate-weight hyaluronic acid on hip and knee cartilage: in-vivo evaluation using T2 mapping. Eur Radiol. 2018;28(6):2345–55.PubMedCrossRef
79.
Welsch GH, Mamisch TC, Zak L, Blanke M, Olk A, Marlovits S, et al. Evaluation of cartilage repair tissue after matrix-associated autologous chondrocyte transplantation using a hyaluronic-based or a collagen-based scaffold with morphological MOCART scoring and biochemical T2 mapping: preliminary results. Am J Sports Med. 2010;38(5):934–42.PubMedCrossRef
80.
81.
82.
83.
Shrikumar A, Greenside P, Kundaje A. Learning important features through propagating activation differences. In: International Conference on Machine Learning. PMLR; 2017. pp. 3145–53.
84.
Springenberg JT, Dosovitskiy A, Brox T, Riedmiller M. Striving for simplicity: the all convolutional net. arXiv preprint arXiv:14126806. 2014.
85.
Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy. 2021;23(1):18.
86.
87.
Torrey L, Shavlik J, Olivas ES, Guerrero JM, Sober MM, Benedito JM, Lopez AS. Handbook of research on machine learning applications and trends. Hershey: Information Science Reference; 2010. pp. 242–64.
88.
Tiulpin A, Saarakkala S. Automatic grading of individual knee osteoarthritis features in plain radiographs using deep convolutional neural networks. Diagnostics. 2020;10(11):932.
89.
90.
Gaj S, Yang M, Nakamura K, Li X. Automated cartilage and meniscus segmentation of knee MRI with conditional generative adversarial networks. Magn Reson Med. 2020;84(1):437–49.PubMedCrossRef
91.
Oneto L, Navarin N, Sperduti A, Anguita D. Recent trends in learning from data: tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) (Studies in Computational Intelligence) Springer; 2020.
92.
Moustakidis S, Papandrianos NI, Christodolou E, Papageorgiou E, Tsaopoulos D. Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness. Neural Computing and Applications. 2020.
Metadaten
Titel
AI MSK clinical applications: cartilage and osteoarthritis
verfasst von
Gabby B. Joseph
Charles E. McCulloch
Jae Ho Sohn
Valentina Pedoia
Sharmila Majumdar
Thomas M. Link
Publikationsdatum
04.11.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Skeletal Radiology / Ausgabe 2/2022
Print ISSN: 0364-2348
Elektronische ISSN: 1432-2161
DOI
https://doi.org/10.1007/s00256-021-03909-2

Weitere Artikel der Ausgabe 2/2022

Skeletal Radiology 2/2022 Zur Ausgabe

Review Article

AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?

Scientific Article

Deep learning approach to predict pain progression in knee osteoarthritis

Review Article

Musculoskeletal trauma and artificial intelligence: current trends and projections

Editorial

Introduction to the special issue on artificial intelligence in musculoskeletal radiology

Scientific Article

Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning

Scientific Article

Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern

Neu im Fachgebiet Radiologie

S3-Leitlinie zu Pankreaskrebs aktualisiert

23.04.2024 Pankreaskarzinom Nachrichten

Die Empfehlungen zur Therapie des Pankreaskarzinoms wurden um zwei Off-Label-Anwendungen erweitert. Und auch im Bereich der Früherkennung gibt es Aktualisierungen.

Fünf Dinge, die im Kindernotfall besser zu unterlassen sind

18.04.2024 Pädiatrische Notfallmedizin Nachrichten

Im Choosing-Wisely-Programm, das für die deutsche Initiative „Klug entscheiden“ Pate gestanden hat, sind erstmals Empfehlungen zum Umgang mit Notfällen von Kindern erschienen. Fünf Dinge gilt es demnach zu vermeiden.

„Nur wer sich gut aufgehoben fühlt, kann auch für Patientensicherheit sorgen“

13.04.2024 Klinik aktuell Kongressbericht

Die Teilnehmer eines Forums beim DGIM-Kongress waren sich einig: Fehler in der Medizin sind häufig in ungeeigneten Prozessen und mangelnder Kommunikation begründet. Gespräche mit Patienten und im Team können helfen.

Wie toxische Männlichkeit der Gesundheit von Männern schadet

08.04.2024 Andrologie Nachrichten

Stark, erfolgreich, allzeit belastbar – das sind Erwartungen, die Jungen und Männer von der Gesellschaft spüren. Das kann sie „toxisch“ werden lassen – und letztlich sogar der Gesundheit schaden, mahnt Dr. Dirk Sander von der Deutschen Aidshilfe.

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen
Bildnachweise