Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study

In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial...

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Veröffentlicht in:	PloS one Jg. 7; H. 5; S. e37245
Hauptverfasser:	Mavandadi, Sam, Dimitrov, Stoyan, Feng, Steve, Yu, Frank, Sikora, Uzair, Yaglidere, Oguzhan, Padmanabhan, Swati, Nielsen, Karin, Ozcan, Aydogan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 11.05.2012 Public Library of Science (PLoS)
Schlagworte:	Algorithms Artificial Intelligence Automation Blood cells Blood Cells - parasitology Case studies Computer & video games Crowdsourcing Decisions Diagnosis Diagnosis, Computer-Assisted Electrical engineering Engineering Erythrocytes Experts Games, Experimental Humans Identification Image analysis Image Interpretation, Computer-Assisted - methods Image processing Information theory Malaria Malaria - blood Malaria - diagnosis Malaria - parasitology Medical diagnosis Medical imaging equipment Medicine Microscopic analysis Microscopy Parasites Pattern Recognition, Automated Pattern Recognition, Visual Physics Problem Solving Social and Behavioral Sciences Telemedicine Vector-borne diseases Vegetables Video Games Vision systems Visual discrimination learning Los Angeles California United States > US California
ISSN:	1932-6203, 1932-6203
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Abstract	In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.
AbstractList	In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional. In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.In this work we investigate whether the innate visual recognition and learning capabilities of untrained humans can be used in conducting reliable microscopic analysis of biomedical samples toward diagnosis. For this purpose, we designed entertaining digital games that are interfaced with artificial learning and processing back-ends to demonstrate that in the case of binary medical diagnostics decisions (e.g., infected vs. uninfected), with the use of crowd-sourced games it is possible to approach the accuracy of medical experts in making such diagnoses. Specifically, using non-expert gamers we report diagnosis of malaria infected red blood cells with an accuracy that is within 1.25% of the diagnostics decisions made by a trained medical professional.
Audience	Academic
Author	Ozcan, Aydogan Yaglidere, Oguzhan Mavandadi, Sam Yu, Frank Padmanabhan, Swati Dimitrov, Stoyan Sikora, Uzair Nielsen, Karin Feng, Steve
AuthorAffiliation	1 Electrical Engineering Department, University of California Los Angeles, Los Angeles, California, United States of America 4 California NanoSystems Institute, University of California Los Angeles, Los Angeles, California, United States of America Centro de Pesquisa Rene Rachou/Fundação Oswaldo Cruz (Fiocruz-Minas), Brazil 3 Bioengineering Department, University of California Los Angeles, Los Angeles, California, United States of America 2 Division of Infectious Diseases, Department of Pediatrics, School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America 5 Department of Surgery, School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America
AuthorAffiliation_xml	– name: 3 Bioengineering Department, University of California Los Angeles, Los Angeles, California, United States of America – name: 5 Department of Surgery, School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America – name: 1 Electrical Engineering Department, University of California Los Angeles, Los Angeles, California, United States of America – name: 2 Division of Infectious Diseases, Department of Pediatrics, School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America – name: 4 California NanoSystems Institute, University of California Los Angeles, Los Angeles, California, United States of America – name: Centro de Pesquisa Rene Rachou/Fundação Oswaldo Cruz (Fiocruz-Minas), Brazil
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/22606353$$D View this record in MEDLINE/PubMed
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Copyright	COPYRIGHT 2012 Public Library of Science 2012 Mavandadi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Mavandadi et al. 2012
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Wrote the paper: SM AO. Designed and developed the theoretical framework and algorithms; contributed to the overall design of the games and related infrastructure; conducted the analysis and interpretation of the data and results: SM. Contributed to the development and implementation of the games and related infrastructure: SD SF FY. Contributed to the acquisition and compilation of microscopic images: US OY SP. Contributed to the medical implementation of the presented platform: KN. Conceived the idea, supervised the project, and contributed to the overall design of the games, related infrastructure and theory as well as to the analysis and interpretation of the data and the results: AO.
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SubjectTerms	Algorithms Artificial Intelligence Automation Blood cells Blood Cells - parasitology Case studies Computer & video games Crowdsourcing Decisions Diagnosis Diagnosis, Computer-Assisted Electrical engineering Engineering Erythrocytes Experts Games, Experimental Humans Identification Image analysis Image Interpretation, Computer-Assisted - methods Image processing Information theory Malaria Malaria - blood Malaria - diagnosis Malaria - parasitology Medical diagnosis Medical imaging equipment Medicine Microscopic analysis Microscopy Parasites Pattern Recognition, Automated Pattern Recognition, Visual Physics Problem Solving Social and Behavioral Sciences Telemedicine Vector-borne diseases Vegetables Video Games Vision systems Visual discrimination learning
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Title	Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study
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