Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study
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{"title"=>"Distributed medical image analysis and diagnosis through crowd-sourced games: A malaria case study", "type"=>"journal", "authors"=>[{"first_name"=>"Sam", "last_name"=>"Mavandadi", "scopus_author_id"=>"6505893736"}, {"first_name"=>"Stoyan", "last_name"=>"Dimitrov", "scopus_author_id"=>"55216794800"}, {"first_name"=>"Steve", "last_name"=>"Feng", "scopus_author_id"=>"53663536400"}, {"first_name"=>"Frank", "last_name"=>"Yu", "scopus_author_id"=>"53664875800"}, {"first_name"=>"Uzair", "last_name"=>"Sikora", "scopus_author_id"=>"37073390600"}, {"first_name"=>"Oguzhan", "last_name"=>"Yaglidere", "scopus_author_id"=>"36146580900"}, {"first_name"=>"Swati", "last_name"=>"Padmanabhan", "scopus_author_id"=>"55216725700"}, {"first_name"=>"Karin", "last_name"=>"Nielsen", "scopus_author_id"=>"7401572566"}, {"first_name"=>"Aydogan", "last_name"=>"Ozcan", "scopus_author_id"=>"7005667692"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84861006684", "sgr"=>"84861006684", "pui"=>"364803099", "isbn"=>"1932-6203; 1932-6203", "pmid"=>"22606353", "doi"=>"10.1371/journal.pone.0037245"}, "id"=>"45b1fa0b-79a4-37a4-aff3-50de341a9993", "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.", "link"=>"http://www.mendeley.com/research/distributed-medical-image-analysis-diagnosis-through-crowdsourced-games-malaria-case-study-1", "reader_count"=>91, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>2, "Student > Master"=>18, "Other"=>10, "Student > Bachelor"=>7, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>19, "Student > Postgraduate"=>2, "Student > Master"=>18, "Other"=>10, "Student > Bachelor"=>7, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>14, "Unspecified"=>3, "Agricultural and Biological Sciences"=>19, "Medicine and Dentistry"=>15, "Arts and Humanities"=>2, "Chemical Engineering"=>1, "Physics and Astronomy"=>1, "Psychology"=>4, "Chemistry"=>2, "Social Sciences"=>3, "Computer Science"=>24, "Economics, Econometrics and Finance"=>3}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>14}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>4}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>19}, "Computer Science"=>{"Computer Science"=>24}, "Unspecified"=>{"Unspecified"=>3}, "Chemical Engineering"=>{"Chemical Engineering"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "Korea (South)"=>1, "United States"=>7, "Finland"=>1, "Denmark"=>1, "United Kingdom"=>3, "South Africa"=>1, "Israel"=>1, "Chile"=>1, "Switzerland"=>1, "Spain"=>2}, "group_count"=>6}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/640378"], "description"=>"<p>The plots show the worst case scenarios where the diagnoses from the worst performing players are used to generate an overall diagnosis for each RBC in the game. Note that the specificity (or true negative rate) is always very high for the gamers, and does not improve much as more gamers are added to the mix. However, the sensitivity (or true positive rate) benefits the most as more players are added, and climbs above 95% once 15-gamers form the crowd. The accuracy also increases as more players are added, but since it reflects both the specificity and the sensitivity its increase is not as drastic as that of the sensitivity.</p>", "links"=>[], "tags"=>["gamer"], "article_id"=>310869, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.g005", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_Crowd_Effect_gamer_performance_results_for_experiment_5_/310869", "title"=>"The Crowd Effect: gamer performance results for experiment #5.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-11 00:14:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/640040"], "description"=>"<p>Malaria Diagnosis Game Interface: the gamer can use the syringe tool to “kill” infected RBCs or use the blood bank button to “bank/collect” all the healthy RBCs.</p>", "links"=>[], "tags"=>["gamer", "syringe", "infected", "rbcs"], "article_id"=>310518, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.g002", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Malaria_Diagnosis_Game_Interface_the_gamer_can_use_the_syringe_tool_to_8220_kill_8221_infected_RBCs_or_use_the_blood_bank_button_to_8220_bank_collect_8221_all_the_healthy_RBCs_/310518", "title"=>"Malaria Diagnosis Game Interface: the gamer can use the syringe tool to “kill” infected RBCs or use the blood bank button to “bank/collect” all the healthy RBCs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-11 00:08:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/330584", "https://ndownloader.figshare.com/files/330623", "https://ndownloader.figshare.com/files/330732", "https://ndownloader.figshare.com/files/330852", "https://ndownloader.figshare.com/files/330931", "https://ndownloader.figshare.com/files/330971"], "description"=>"<div><p>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.</p> </div>", "links"=>[], "tags"=>["distributed", "crowd-sourced", "malaria"], "article_id"=>125287, "categories"=>["Physics", "Cancer", "Cell Biology"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0037245.s001", "https://dx.doi.org/10.1371/journal.pone.0037245.s002", "https://dx.doi.org/10.1371/journal.pone.0037245.s003", "https://dx.doi.org/10.1371/journal.pone.0037245.s004", "https://dx.doi.org/10.1371/journal.pone.0037245.s005", "https://dx.doi.org/10.1371/journal.pone.0037245.s006"], "stats"=>{"downloads"=>14, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Distributed_Medical_Image_Analysis_and_Diagnosis_through_Crowd_Sourced_Games_A_Malaria_Case_Study/125287", "title"=>"Distributed Medical Image Analysis and Diagnosis through Crowd-Sourced Games: A Malaria Case Study", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-05-11 01:28:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/640128"], "description"=>"<p>The images are treated as a sequence of binary values that are broadcast by the server. The gamers are effectively noisy repeaters that in the most ideal case output the correct symbol for the inputs that they receive. Each repeater transmits its own noisy version of the same input symbol to a decoder. The decoder combines all the received repeater outputs and decodes a final output z<sub>i</sub>, which ideally will be the correct label/diagnosis for the input images. The repeaters can be modelled as Binary Communication Channels (top-left). <i>p<sub>ij</sub></i> corresponds to the probability of receiving symbol <i>j</i> when in fact symbol <i>i</i> was transmitted.</p>", "links"=>[], "tags"=>["gaming"], "article_id"=>310616, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.g003", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_the_gaming_analysis_framework_/310616", "title"=>"Overview of the gaming analysis framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-11 00:10:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/640215"], "description"=>"<p>As new images are generated, they are diagnosed by pre-trained machine learning algorithms. The confidence of these algorithms in their decisions determines whether the images should be passed on to human gamers or not. Once the <i>difficult-to-diagnose</i> images for these algorithms are crowd-sourced and are diagnosed by the human gamers, they are merged with the <i>easy-to-diagnose</i> images to compute the final diagnostics results. The data is then fed back through the system and added to the training dataset used by the machine learning algorithms. During each cycle, self-learning algorithms will improve as a result of added training data. ‘T’ refers to a threshold value.</p>", "links"=>[], "tags"=>["hybrid", "diagnostics"], "article_id"=>310697, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.g004", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_hybrid_human_machine_diagnostics_framework_/310697", "title"=>"The hybrid (human + machine) diagnostics framework.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-11 00:11:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/639941"], "description"=>"<p><b>A</b>) Biomedical data (e.g., images of thin blood smear samples) from individual light microscopes all around the world are transmitted to data centres where they are pre-processed and digitally distributed among gamers, which in turn diagnose and transmit their responses back. These individual results of the gamers are then fused toward a final diagnosis, the result of which is transmitted back to the point-of-care or the clinic/hospital. In the map above, orange-coloured regions show locations where risk of contraction of malaria still exists. <b>B</b>) Block diagram of the presented platform.</p>", "links"=>[], "tags"=>["Infectious diseases", "pathology", "physics"], "article_id"=>310427, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proposed_platform_/310427", "title"=>"Proposed platform.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-05-11 00:07:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/640485"], "description"=>"<p>Summary of experimental results for diagnosis of malaria infected red blood cells.</p>", "links"=>[], "tags"=>["malaria", "infected"], "article_id"=>310972, "categories"=>["Physics", "Cell Biology", "Infectious Diseases"], "users"=>["Sam Mavandadi", "Stoyan Dimitrov", "Steve Feng", "Frank Yu", "Uzair Sikora", "Oguzhan Yaglidere", "Swati Padmanabhan", "Karin Nielsen", "Aydogan Ozcan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037245.t001", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_experimental_results_for_diagnosis_of_malaria_infected_red_blood_cells_/310972", "title"=>"Summary of experimental results for diagnosis of malaria infected red blood cells.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-05-11 00:16:12"}

PMC Usage Stats | Further Information

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Relative Metric

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