Skip to main content
Top
Published in: International Journal of Diabetes in Developing Countries 1/2019

01-01-2019 | Review Article

A critical review of red lesion detection algorithms using fundus images

Authors: Shilpa Joshi, P. T. Karule

Published in: International Journal of Diabetes in Developing Countries | Issue 1/2019

Login to get access

Abstract

There are two types of clinical signs of diabetic retinopathy: red lesions and bright lesions. Red lesions which include microaneurysms are early signs of diabetic retinopathy. Microaneurysms are clinically important initial qualities of retinopathy. Their number increases with the severity of retinopathy. Hemorrhages are also signs of diabetic retinopathy which appear after microaneurysms. This work has concentrated on the review of the development of several techniques to automate the detection and classification of suggestive features of red lesions of first retinopathy progress.
Literature
1.
go back to reference Lay BJ (1983). Analyze automatic fluorescein angiograms images and diabetic retinopathy, PhD thesis, Center of Mathematical Morphology, Paris School of Mines. Lay BJ (1983). Analyze automatic fluorescein angiograms images and diabetic retinopathy, PhD thesis, Center of Mathematical Morphology, Paris School of Mines.
2.
go back to reference Oien GE, Osnes P. Diabetic retinopathy: automatic detection of early symptoms from retinal images, Proceedings Norwegian Signal Processing Symposium. 1995;pp. 7–9. https://doi.org/10.1.1.40.3374. Oien GE, Osnes P. Diabetic retinopathy: automatic detection of early symptoms from retinal images, Proceedings Norwegian Signal Processing Symposium. 1995;pp. 7–9. https://​doi.​org/​10.​1.​1.​40.​3374.​
3.
go back to reference Spencer T, Phillips S, McHardy K, Forrester J. Automated detection and quantification of MAs in fluorescein angiograms. Graefes Archive for Clinical and Experimental Ophthalmology, Springer 1991. pp. 36–41. https://doi.org/10.1007/BF00166760. Spencer T, Phillips S, McHardy K, Forrester J. Automated detection and quantification of MAs in fluorescein angiograms. Graefes Archive for Clinical and Experimental Ophthalmology, Springer 1991. pp. 36–41. https://​doi.​org/​10.​1007/​BF00166760.
4.
go back to reference Frame A, Undrill PE, Cree MJ, Olson JA, Forrester J. A comparison of computer-based classification methods applied to the detection of MAs in ophthalmic fluorescein angiograms. Comput BioL Med. 1998;28:225–38.CrossRefPubMed Frame A, Undrill PE, Cree MJ, Olson JA, Forrester J. A comparison of computer-based classification methods applied to the detection of MAs in ophthalmic fluorescein angiograms. Comput BioL Med. 1998;28:225–38.CrossRefPubMed
7.
go back to reference Karnowski TP, Govindasamy VP, Tobin KW, Chaum E, Abramoff MD. Retina lesions and microaneurysms segmentation using morphological reconstruction methods with ground-truth data, Conf. Proc. IEEE Engineering Med Biology Society, 2008 pp. 5433–5436. Karnowski TP, Govindasamy VP, Tobin KW, Chaum E, Abramoff MD. Retina lesions and microaneurysms segmentation using morphological reconstruction methods with ground-truth data, Conf. Proc. IEEE Engineering Med Biology Society, 2008 pp. 5433–5436.
9.
go back to reference Matei D, Matei R. Detection of diabetic symptoms in retina images using analog algorithms, World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA ©2008, ISPRA'08 2010 pp. 224–227. Matei D, Matei R. Detection of diabetic symptoms in retina images using analog algorithms, World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA ©2008, ISPRA'08 2010 pp. 224–227.
10.
11.
go back to reference Jaafar H. F., Nandi A.K. and Nuaimy W.. Automatic detection of red lesions from digital color fundus photographs, Annual Int. Conf. Of the IEEE EMBS 2011 pp. 6232–6235. Jaafar H. F., Nandi A.K. and Nuaimy W.. Automatic detection of red lesions from digital color fundus photographs, Annual Int. Conf. Of the IEEE EMBS 2011 pp. 6232–6235.
12.
go back to reference Goldbaum M, Moezzi S, Taylor A, Chatterjee S, Boyd J, Hunter E, Jain R. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images, IEEE Int. Conf. on Image Processing, 1996 pp. 695–698. https://doi.org/10.1109/ICIP.1996.560760. Goldbaum M, Moezzi S, Taylor A, Chatterjee S, Boyd J, Hunter E, Jain R. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images, IEEE Int. Conf. on Image Processing, 1996 pp. 695–698. https://​doi.​org/​10.​1109/​ICIP.​1996.​560760.
15.
go back to reference Niemeijer M, Ginneken BV, Staal J, Suttorp-Schulten MS. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging. 2005;24(5):584–92.CrossRefPubMed Niemeijer M, Ginneken BV, Staal J, Suttorp-Schulten MS. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging. 2005;24(5):584–92.CrossRefPubMed
19.
go back to reference Walter T. Application of mathematical morphology a diagnostic retinal automatic images fluorescein angiographic and color diabetic retinopathy images, PhD thesis. 2003. Walter T. Application of mathematical morphology a diagnostic retinal automatic images fluorescein angiographic and color diabetic retinopathy images, PhD thesis. 2003.
24.
go back to reference Li T, Niemeijer M, Abramoff MD. Splat feature classification: detection of the presence of large retinal hemorrhages, IEEE Symp on Bio Medical Imaging, 2011 pp 681–684. Li T, Niemeijer M, Abramoff MD. Splat feature classification: detection of the presence of large retinal hemorrhages, IEEE Symp on Bio Medical Imaging, 2011 pp 681–684.
25.
go back to reference Niemeijer M, Ginneken B, Cree MJ, Mizutani A, Quellec G, Sánchez C, et al. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging. 2010;29:185–95. Niemeijer M, Ginneken B, Cree MJ, Mizutani A, Quellec G, Sánchez C, et al. Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans Med Imaging. 2010;29:185–95.
26.
go back to reference Kande GB, Tirumala SS, Subbaiah PV. Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging Springer. 2010;23:430–7.CrossRef Kande GB, Tirumala SS, Subbaiah PV. Automatic detection of microaneurysms and hemorrhages in digital fundus images. J Digit Imaging Springer. 2010;23:430–7.CrossRef
28.
go back to reference Garcia M, Sanchez C, Lopez I, Diez A. Automatic detection of red lesions in retinal images using a multilayer perceptron neural network. Conf Proc IEEE Eng Med Biol Soc. 2008:1085–93. Garcia M, Sanchez C, Lopez I, Diez A. Automatic detection of red lesions in retinal images using a multilayer perceptron neural network. Conf Proc IEEE Eng Med Biol Soc. 2008:1085–93.
30.
go back to reference Wenhua X., Faling Y. and Guohua C.. Detection of microaneurysms in digital fundus images based on SVM, IEEE Int. Conference on Oxide Materials for Electronic Engineering (OMEE), 2012 pp. 104–108. Wenhua X., Faling Y. and Guohua C.. Detection of microaneurysms in digital fundus images based on SVM, IEEE Int. Conference on Oxide Materials for Electronic Engineering (OMEE), 2012 pp. 104–108.
32.
go back to reference Nagayoshi H, Hiramatsu Y, Kagehiro T, Mizuno Y, Himaga M, Sato S. Detection of lesions from fundus images for diagnosis of diabetic retinopathy. IEICE. 2005:61–6. Nagayoshi H, Hiramatsu Y, Kagehiro T, Mizuno Y, Himaga M, Sato S. Detection of lesions from fundus images for diagnosis of diabetic retinopathy. IEICE. 2005:61–6.
34.
go back to reference Roychoudhari S, Dara DK, Keshab KP. DREAM: diabetic retinopathy analysis using machine learning. IEEE J BiomedHealth Info. 2014:1717–29. Roychoudhari S, Dara DK, Keshab KP. DREAM: diabetic retinopathy analysis using machine learning. IEEE J BiomedHealth Info. 2014:1717–29.
35.
go back to reference Quellec G, Lamard M, Josselin P M, Cazuguel G, Cochener B, Roux C. Detection of lesions in retina photographs based on the wavelet transform, International Conference of the IEEE Engineering in Medicine and Biology Society. 2006 pp. 2618–2621. Quellec G, Lamard M, Josselin P M, Cazuguel G, Cochener B, Roux C. Detection of lesions in retina photographs based on the wavelet transform, International Conference of the IEEE Engineering in Medicine and Biology Society. 2006 pp. 2618–2621.
36.
go back to reference Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H. Improvement of automated detection method of hemorrhages in fundus images. IEEE EMBS. 2008:5429–32. Hatanaka Y, Nakagawa T, Hayashi Y, Hara T, Fujita H. Improvement of automated detection method of hemorrhages in fundus images. IEEE EMBS. 2008:5429–32.
37.
go back to reference Hatanaka Y, Nakagawa T, Hayashi Y, Mizukusa Y, Fujita A, Kahase K, Fujita H. CAD scheme to detect hemorrhages and exudates in ocular fundus images, Proc. SPIE 6514, Medical Imaging, Computer Aided Diagnosis, 2007. https://doi.org/10.1117/12.708367. Hatanaka Y, Nakagawa T, Hayashi Y, Mizukusa Y, Fujita A, Kahase K, Fujita H. CAD scheme to detect hemorrhages and exudates in ocular fundus images, Proc. SPIE 6514, Medical Imaging, Computer Aided Diagnosis, 2007. https://​doi.​org/​10.​1117/​12.​708367.
38.
go back to reference Lee SC, Lee ET, Kingsley RM, Wang Y, Russell D, Klein R, Warn A. Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch Ophthalmol. 2001;119(4):509–15. Lee SC, Lee ET, Kingsley RM, Wang Y, Russell D, Klein R, Warn A. Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch Ophthalmol. 2001;119(4):509–15.
41.
go back to reference Sherif A. Microaneurysms detection using vessels removal and circular Hough transform, IEEE Proc 19th Natl Radio Science Conference, Egypt 2002 1-2:421–426. Sherif A. Microaneurysms detection using vessels removal and circular Hough transform, IEEE Proc 19th Natl Radio Science Conference, Egypt 2002 1-2:421–426.
42.
go back to reference Raman B., Bursell E. S., Wilson M., Zamora G., Benche I., Nemeth S. C. and Soliz P.. The effects of spatial resolution on an automated diabetic retinopathy screening system's performance in detecting microaneurysms for diabetic retinopathy, Proceedings of 17th IEEE Symposium on Computer-Based Medical Systems, 2004 pp.128–133. Raman B., Bursell E. S., Wilson M., Zamora G., Benche I., Nemeth S. C. and Soliz P.. The effects of spatial resolution on an automated diabetic retinopathy screening system's performance in detecting microaneurysms for diabetic retinopathy, Proceedings of 17th IEEE Symposium on Computer-Based Medical Systems, 2004 pp.128–133.
48.
go back to reference Mendonça A., Campilho A., Nunes J. Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients. Proc. ICIAP, 10th Int. Conference Image Analysis Process, 1999 p 728. Mendonça A., Campilho A., Nunes J. Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients. Proc. ICIAP, 10th Int. Conference Image Analysis Process, 1999 p 728.
50.
go back to reference Bhalerao A, Patanaik A, Anand S, Saravanan P. Robust detection of microaneurysms for sight threatening retinopathy screening, In: 6th Indian Conference on Computer Vision, Graphics & Image Processing 2008 pp 520–527. Bhalerao A, Patanaik A, Anand S, Saravanan P. Robust detection of microaneurysms for sight threatening retinopathy screening, In: 6th Indian Conference on Computer Vision, Graphics & Image Processing 2008 pp 520–527.
51.
go back to reference Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, et al. Automated detection of diabetic retinopathy on digital fundus image. Int J Diabet Med. 2002;19:105–12. Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, et al. Automated detection of diabetic retinopathy on digital fundus image. Int J Diabet Med. 2002;19:105–12.
52.
go back to reference Streeter L. and Cree M. J.. Microaneurysms detection in color fundus images, Image and Vision Computing NZ, 2003 pp 280–285. Streeter L. and Cree M. J.. Microaneurysms detection in color fundus images, Image and Vision Computing NZ, 2003 pp 280–285.
53.
go back to reference Serrano C, Acha B, Revuelto S. 2D adaptive filtering and region growing algorithm for the detection of microaneurysms. Proc SPIE Med Imaging: Process. 2007;5370:1924–31.CrossRef Serrano C, Acha B, Revuelto S. 2D adaptive filtering and region growing algorithm for the detection of microaneurysms. Proc SPIE Med Imaging: Process. 2007;5370:1924–31.CrossRef
54.
go back to reference Fleming A D, Philip S, Goatman K A. Automated microaneurysms detection using local contrast normalization and local vessel detection, IEEE Trans in Medical Imag. 2006 pp. 1223–1232. Fleming A D, Philip S, Goatman K A. Automated microaneurysms detection using local contrast normalization and local vessel detection, IEEE Trans in Medical Imag. 2006 pp. 1223–1232.
55.
go back to reference Marino C, Ares E, Penedo ME, Ortega M, Barreira N, Gomez F. Automated three stage red lesions detection in digital color fundus images. WSEAS Trans Comput. 2008;7:207–15. Marino C, Ares E, Penedo ME, Ortega M, Barreira N, Gomez F. Automated three stage red lesions detection in digital color fundus images. WSEAS Trans Comput. 2008;7:207–15.
56.
go back to reference Lalonde M, Laliberté F, Gagnon L. RetsoftPlus: a tool for retinal image analysis. Proc 17th IEEE Symp Computer-Based Med System (CBMS’04). Bethesda, MD, USA, 2004 pp. 542–547. Lalonde M, Laliberté F, Gagnon L. RetsoftPlus: a tool for retinal image analysis. Proc 17th IEEE Symp Computer-Based Med System (CBMS’04). Bethesda, MD, USA, 2004 pp. 542–547.
57.
go back to reference Abramoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russel SR, Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care. 2008;31:193–8. https://doi.org/10.2337/dc07-1312.CrossRefPubMed Abramoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russel SR, Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care. 2008;31:193–8. https://​doi.​org/​10.​2337/​dc07-1312.CrossRefPubMed
58.
go back to reference Kauppi T., Kalesnykiene V., Kmrinen J. K., Lensu L, Sorr I., Raninen A., Voutilainen R., Uusitalo H., Klviinen H. and Pietil J.. Diaretdb1 diabetic retinopathy database and evaluation protocol, Proc. 11th Conf. Med. Image Understand Analysis 2007 pp. 61–65. Kauppi T., Kalesnykiene V., Kmrinen J. K., Lensu L, Sorr I., Raninen A., Voutilainen R., Uusitalo H., Klviinen H. and Pietil J.. Diaretdb1 diabetic retinopathy database and evaluation protocol, Proc. 11th Conf. Med. Image Understand Analysis 2007 pp. 61–65.
60.
go back to reference Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using artificial neural network: a screening tool. Br J Ophthalmol. 1996;80:940–4.CrossRefPubMedPubMedCentral Gardner GG, Keating D, Williamson TH, Elliott AT. Automatic detection of diabetic retinopathy using artificial neural network: a screening tool. Br J Ophthalmol. 1996;80:940–4.CrossRefPubMedPubMedCentral
61.
go back to reference Spencer T, Olson JA, McHardy K, Sharp P, Forrester J. An image-processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Computer Biomed Res. 1996;29:284–302.CrossRef Spencer T, Olson JA, McHardy K, Sharp P, Forrester J. An image-processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Computer Biomed Res. 1996;29:284–302.CrossRef
62.
go back to reference Hipwell JH, Strachan F, Olson JA, McHardy KC, Forrester J. Automated detection of microaneurysms in digital red-free photographs: a diabetic screening tool. Diabet Med. 2000;17(8):588–94.CrossRefPubMed Hipwell JH, Strachan F, Olson JA, McHardy KC, Forrester J. Automated detection of microaneurysms in digital red-free photographs: a diabetic screening tool. Diabet Med. 2000;17(8):588–94.CrossRefPubMed
64.
go back to reference Grisan E., Ruggeri A.. A hierarchical Bayesian classification for non-vascular lesions detection in fundus images, in EMBEC’05, Springer, 3rd European Medical and Biological Engineering Conference 2005 pp. 234–240. Grisan E., Ruggeri A.. A hierarchical Bayesian classification for non-vascular lesions detection in fundus images, in EMBEC’05, Springer, 3rd European Medical and Biological Engineering Conference 2005 pp. 234–240.
67.
go back to reference Dupas B, Walter T, Erginay A, Ordonez R, Deb-Joardar N, Gain P, et al. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, hemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab. 2010;36:213–20. https://doi.org/10.1016/j.diabet2010.01.02. Dupas B, Walter T, Erginay A, Ordonez R, Deb-Joardar N, Gain P, et al. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, hemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab. 2010;36:213–20. https://​doi.​org/​10.​1016/​j.​diabet2010.​01.​02.
68.
go back to reference Antal B., Hajdu A. Improving microaneurysms detection in color fundus images using an optimal combination of preprocessing methods and candidate extractor, 18th European Signal Processing Conference, IEEE, EUSIPCO 2010 1224–1228. Antal B., Hajdu A. Improving microaneurysms detection in color fundus images using an optimal combination of preprocessing methods and candidate extractor, 18th European Signal Processing Conference, IEEE, EUSIPCO 2010 1224–1228.
69.
70.
go back to reference Giancardo L, Meriaudeau F, Karnowski T, Tobin K. Validation of microaneurysms-based diabetic retinopathy screening across retina fundus datasets. IEEE CBMS. 2013:125–31. Giancardo L, Meriaudeau F, Karnowski T, Tobin K. Validation of microaneurysms-based diabetic retinopathy screening across retina fundus datasets. IEEE CBMS. 2013:125–31.
71.
go back to reference Tang L, Niemeijer M, Reinhardt J, Garvin M. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging. 2013;32(2):364–75.CrossRefPubMed Tang L, Niemeijer M, Reinhardt J, Garvin M. Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Trans Med Imaging. 2013;32(2):364–75.CrossRefPubMed
73.
go back to reference Manjiri P, Ramesh M, Rajput Y, Saswade M, Deshpande N. Automated localization of optic disk, detection of microaneurysms and extraction of blood vessels to bypass angiography, Proc. of Int. Conference on Frontiers of Intelligent Computing: Theory and Application, https://link.springer.com/book/10.1007/978-3-319-11933-5, 2014 pp. 579–587. Manjiri P, Ramesh M, Rajput Y, Saswade M, Deshpande N. Automated localization of optic disk, detection of microaneurysms and extraction of blood vessels to bypass angiography, Proc. of Int. Conference on Frontiers of Intelligent Computing: Theory and Application, https://​link.​springer.​com/​book/​10.​1007/​978-3-319-11933-5, 2014 pp. 579–587.
74.
75.
go back to reference Varsanyi P, Fegyvari Z, Sergyan S, Vamossy Z. Manual microaneurysms detection support with size and shape-based detection, IEEE Symposium Application of Computer Intelligence and Informatics 2014 pp. 361–365. Varsanyi P, Fegyvari Z, Sergyan S, Vamossy Z. Manual microaneurysms detection support with size and shape-based detection, IEEE Symposium Application of Computer Intelligence and Informatics 2014 pp. 361–365.
76.
go back to reference Manoj Kumar SB, Manjunath R, Sheshadri HS. Feature extraction from the fundus images for the diagnosis of diabetic retinopathy. International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), IEEE 2015 pp.240–245. Manoj Kumar SB, Manjunath R, Sheshadri HS. Feature extraction from the fundus images for the diagnosis of diabetic retinopathy. International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), IEEE 2015 pp.240–245.
77.
go back to reference Das V, Puhan N B, Panda R. Entropy thresholding based microaneurysms detection in fundus images, Conference on Computer Vision, Pattern Recognition, Image Processing Graphics IEEE 2015 pp. 1–4. Das V, Puhan N B, Panda R. Entropy thresholding based microaneurysms detection in fundus images, Conference on Computer Vision, Pattern Recognition, Image Processing Graphics IEEE 2015 pp. 1–4.
78.
go back to reference Bharali P, Medhi J P and Nirmala S.R., (2015). Detection of hemorrhages in diabetic retinopathy analysis using color fundus images, IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), IEEE, pp. 237–242. Bharali P, Medhi J P and Nirmala S.R., (2015). Detection of hemorrhages in diabetic retinopathy analysis using color fundus images, IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), IEEE, pp. 237–242.
79.
go back to reference Mane V, Kawadiwale RB, Jadhav DV. Detection of red lesions in diabetic retinopathy affected fundus images. Adv Comput IEEE. 2015:56–60. Mane V, Kawadiwale RB, Jadhav DV. Detection of red lesions in diabetic retinopathy affected fundus images. Adv Comput IEEE. 2015:56–60.
80.
go back to reference Shan J, Li L. Deep learning method for microaneurysms detection in fundus images, IEEE First Conference on Connected Health: Application, Systems and Engineering Technologies 2016 pp.357–358. Shan J, Li L. Deep learning method for microaneurysms detection in fundus images, IEEE First Conference on Connected Health: Application, Systems and Engineering Technologies 2016 pp.357–358.
81.
go back to reference Mohammad N, Zaid U, Supriyanto E. Automated detection of microaneurysms for fundus images. IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Conference, 2016 pp. 600–605. Mohammad N, Zaid U, Supriyanto E. Automated detection of microaneurysms for fundus images. IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Conference, 2016 pp. 600–605.
Metadata
Title
A critical review of red lesion detection algorithms using fundus images
Authors
Shilpa Joshi
P. T. Karule
Publication date
01-01-2019
Publisher
Springer India
Published in
International Journal of Diabetes in Developing Countries / Issue 1/2019
Print ISSN: 0973-3930
Electronic ISSN: 1998-3832
DOI
https://doi.org/10.1007/s13410-018-0632-3

Other articles of this Issue 1/2019

International Journal of Diabetes in Developing Countries 1/2019 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.