<\/strong><\/span><\/h5>\n<\/strong><\/span><\/h5>\n<\/strong><\/span><\/h5>\n<\/strong><\/span><\/h5>\nProf. Dr. Zurac Sabina<\/strong><\/span>
\nUniversitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti
\n Spitalului Clinic Colentina, Bucure\u0219ti
\n<\/strong><\/span><\/h5>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.17.0″ global_colors_info=”{}”]<\/p>\n
Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*autori cu contributie egala<\/span><\/p>\n1 Spitalul Clinic Colentina
2 Universitatea de Medicin\u0103 \u0219i Farmacie Carol Davila Bucuresti
3 Zaya Artificial Intelligence SRL
4 Universitatea Politehnic\u0103 Bucuresti<\/p>\n
Inteligen\u021ba artificial\u0103, tuberculoza, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculoza reprezint\u0103 prima cauz\u0103 de deces prin boal\u0103 infec\u021bioas\u0103. Ideal, diagnosticul TBC se bazeaz\u0103 pe identificarea Mycobacteriilor. Deoarece Mycobacterium tuberculosis este o bacterie de 2-4\/0,2-0,5 microni, c\u0103utarea ei \u00een lamele colorate Ziehl Nielsen (ZN) cu obiectiv microscopic 40X (diametru 0,5 mm) dureaz\u0103 ore; inteligen\u021ba artificial\u0103 poate oferi solu\u021bii viabile \u00een acest domeniu.
Echipa noastr\u0103 include patologi, ingineri de machine learning \u0219i exper\u021bi \u00een IT \u0219i economie. Setul de date de antrenare include 500.000 de casete de delimitare pozitive \u0219i 1.000.000 negative provenite din lamele ZN a 81 de blocuri de parafin\u0103 (21 pozitive, 60 negative); toate lamele au fost scanate folosind scanere manuale \u0219i automate; 7 patologi au notat zone pozitive \u0219i negative. Echipa de machine learning a propus mai multe tehnici de augmentare a imaginii cuplate cu diferite arhitecturi de computer vision. Setul de date de testare include 23 de lame ZN pozitive \u0219i 23 negative (46 de blocuri); lamele au fost scanate integral (whole slide images – WSI). 4 echipe de patologi au evaluat separat WSI-urile \u0219i lamele; WSI-urile au fost \u00eenc\u0103rcate pentru analiz\u0103 automat\u0103.
Analiza automat\u0103 a WSI genereaz\u0103 un raport care indic\u0103 zonele susceptibile de a prezenta micobacterii. Arhitectura noastr\u0103 prezinta rezultate foarte bune (0.9020 pe curba ROC). Rezultatele testului final (ma\u0219in\u0103 versus om care evalueaz\u0103 WSI, ma\u0219in\u0103 versus om care evalueaz\u0103 lame \u0219i om care evalueaz\u0103 WSI versus lame) arat\u0103 acurate\u021be peste 90% \u00een toate cazurile.
Software-ul bazat pe inteligen\u021b\u0103 artificial\u0103 pentru analiza automat\u0103 a lamelor colorate ZN prezint\u0103 rezultate fiabile, oferind o economie extraordinar\u0103 \u00een timpul \u0219i efortul patologului.<\/p>\n
Artificial Intelligence in histopathology: Colentina University Hospital and Zaya Artificial Intelligence experience<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*Authors with equal contribution<\/span><\/p>\n1 Colentina University Hospital
2 University of Medicine and Pharmacy Carol Davila Bucharest
3 Zaya Artificial Intelligence SRL
4 University of Polytechnics Bucharest<\/p>\n
Artificial intelligence, tuberculosis, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculosis represents the first cause of death by infectious diseases. Ideally, TB diagnosis relies on Mycobacteria identification. Since Mycobacterium tuberculosis is a 2-4\/0.2-0.5 microns bacterium, its searched in Ziehl Nielsen (ZN) stained-slides with 40X microscopic objective (0.5mm diameter) takes hours; artificial intelligence may offer viable solutions in this field.
Our team includes pathologists from Colentina University Hospital, machine learning engineers from University of Polytechnics Bucharest and experts in IT and economics from Zaya Artificial Intelligence SRL. Our training data set includes 500.000 positive and 1.000.000 negative bounding boxes originating in ZN-stained slides of 81 paraffin blocks (21 positive, 60 negative); all the slides were scanned using manual and automated slide scanners; 7 pathologists annotated positive and negative areas. The machine learning team has proposed several image augmentation techniques coupled with different custom computer vision architectures. Our testing data set includes 23 positive and 23 negative ZN-stained slides (from 46 paraffin blocks); the slides were scanned as whole slide images (WSIs). 4 teams of pathologists separately evaluated the WSIs and the original slides; the WSIs were sent for automatic analysis.
WSIs automatic analysis was followed by a report indicating areas more likely to present Mycobacteria. Our architecture showed very good results (0.9020 on ROC curve). The results of final test (machine versus man evaluating WSIs, machine versus man evaluating slides and man evaluating WSIs versus slides) showed accuracy over 90% in all cases.
AI-based software for automated analysis of ZN-stained slides shows reliable results, offering a tremendous economy in pathologist\u2019s time&effort.<\/p>\n
Scurt CV<\/span><\/p>\nProfessor of Pathology in Faculty of Dental Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d Bucharest, senior pathologist and head of the Department of Pathologicy Colentina University Hospital, Bucharest, Romania.
Specializations in the field of dermatopathology (Austria 2012), molecular biology (Italy 2012), digestive pathology (Bucharest 2016, 2015, 2011, 2010, 2009 and 2008), oral pathology (the Netherlands 2005-2006), immunohistochemistry (Bucharest 2004; Greece 2001 ), lymphoid pathology (Greece 2001), pulmonary pathology (Italy 1999), soft tissue pathology (Greece 1997), liver pathology (Greece 1996)
Member of the European Society of Pathology, International Academy of Pathology, International Society of Dermatopathology, Romanian Society of Dermatooncology, Romanian Association of Immunodermatology, European Academy of Dermatovenerology
Author of over 330 scientific papers presented at national and international congresses and symposiums, over 125 articles published in specialized magazines, 23 specialized chapters \/ monographs. H-index (WOS all data bases) 10; citations: 460.
Researcher \/ manager \/ project manager in 16 research projects
Areas of interest: dermatopathology, oral pathology, infectious pathology, soft tissue tumors<\/p>\n
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Contacteaz\u0103 operatorul PDI 2022<\/p>\n
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Operatorul PDI 2022<\/p>\n
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<\/span><\/p>\n<\/span><\/p>\n<\/span><\/p>\nAdresa: Str. A. Panu nr. 13, Iasi<\/span>
Tel.: 0332.40.88.00-05<\/span>
E-mail: contact@pdi.ro<\/a>
<\/span>Website:\u00a0<\/span>www.eventernet.ro<\/a><\/p>\n[\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”Kaushan Script||||||||” text_text_color=”#f0622a” text_font_size=”30px” header_font=”||||||||” background_layout=”dark” disabled=”on” locked=”off” global_colors_info=”{}”]<\/p>\n
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Prof. Dr. Zurac Sabina Universitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti Spitalului Clinic Colentina, Bucure\u0219ti Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, […]<\/p>\n","protected":false},"author":2,"featured_media":11376,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-8671","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lectori"],"_links":{"self":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/comments?post=8671"}],"version-history":[{"count":9,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions"}],"predecessor-version":[{"id":11377,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions\/11377"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media\/11376"}],"wp:attachment":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media?parent=8671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/categories?post=8671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/tags?post=8671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/strong><\/span><\/h5>\n<\/strong><\/span><\/h5>\nProf. Dr. Zurac Sabina<\/strong><\/span>
\nUniversitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti
\n Spitalului Clinic Colentina, Bucure\u0219ti
\n<\/strong><\/span><\/h5>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.17.0″ global_colors_info=”{}”]<\/p>\n
Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*autori cu contributie egala<\/span><\/p>\n1 Spitalul Clinic Colentina
2 Universitatea de Medicin\u0103 \u0219i Farmacie Carol Davila Bucuresti
3 Zaya Artificial Intelligence SRL
4 Universitatea Politehnic\u0103 Bucuresti<\/p>\n
Inteligen\u021ba artificial\u0103, tuberculoza, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculoza reprezint\u0103 prima cauz\u0103 de deces prin boal\u0103 infec\u021bioas\u0103. Ideal, diagnosticul TBC se bazeaz\u0103 pe identificarea Mycobacteriilor. Deoarece Mycobacterium tuberculosis este o bacterie de 2-4\/0,2-0,5 microni, c\u0103utarea ei \u00een lamele colorate Ziehl Nielsen (ZN) cu obiectiv microscopic 40X (diametru 0,5 mm) dureaz\u0103 ore; inteligen\u021ba artificial\u0103 poate oferi solu\u021bii viabile \u00een acest domeniu.
Echipa noastr\u0103 include patologi, ingineri de machine learning \u0219i exper\u021bi \u00een IT \u0219i economie. Setul de date de antrenare include 500.000 de casete de delimitare pozitive \u0219i 1.000.000 negative provenite din lamele ZN a 81 de blocuri de parafin\u0103 (21 pozitive, 60 negative); toate lamele au fost scanate folosind scanere manuale \u0219i automate; 7 patologi au notat zone pozitive \u0219i negative. Echipa de machine learning a propus mai multe tehnici de augmentare a imaginii cuplate cu diferite arhitecturi de computer vision. Setul de date de testare include 23 de lame ZN pozitive \u0219i 23 negative (46 de blocuri); lamele au fost scanate integral (whole slide images – WSI). 4 echipe de patologi au evaluat separat WSI-urile \u0219i lamele; WSI-urile au fost \u00eenc\u0103rcate pentru analiz\u0103 automat\u0103.
Analiza automat\u0103 a WSI genereaz\u0103 un raport care indic\u0103 zonele susceptibile de a prezenta micobacterii. Arhitectura noastr\u0103 prezinta rezultate foarte bune (0.9020 pe curba ROC). Rezultatele testului final (ma\u0219in\u0103 versus om care evalueaz\u0103 WSI, ma\u0219in\u0103 versus om care evalueaz\u0103 lame \u0219i om care evalueaz\u0103 WSI versus lame) arat\u0103 acurate\u021be peste 90% \u00een toate cazurile.
Software-ul bazat pe inteligen\u021b\u0103 artificial\u0103 pentru analiza automat\u0103 a lamelor colorate ZN prezint\u0103 rezultate fiabile, oferind o economie extraordinar\u0103 \u00een timpul \u0219i efortul patologului.<\/p>\n
Artificial Intelligence in histopathology: Colentina University Hospital and Zaya Artificial Intelligence experience<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*Authors with equal contribution<\/span><\/p>\n1 Colentina University Hospital
2 University of Medicine and Pharmacy Carol Davila Bucharest
3 Zaya Artificial Intelligence SRL
4 University of Polytechnics Bucharest<\/p>\n
Artificial intelligence, tuberculosis, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculosis represents the first cause of death by infectious diseases. Ideally, TB diagnosis relies on Mycobacteria identification. Since Mycobacterium tuberculosis is a 2-4\/0.2-0.5 microns bacterium, its searched in Ziehl Nielsen (ZN) stained-slides with 40X microscopic objective (0.5mm diameter) takes hours; artificial intelligence may offer viable solutions in this field.
Our team includes pathologists from Colentina University Hospital, machine learning engineers from University of Polytechnics Bucharest and experts in IT and economics from Zaya Artificial Intelligence SRL. Our training data set includes 500.000 positive and 1.000.000 negative bounding boxes originating in ZN-stained slides of 81 paraffin blocks (21 positive, 60 negative); all the slides were scanned using manual and automated slide scanners; 7 pathologists annotated positive and negative areas. The machine learning team has proposed several image augmentation techniques coupled with different custom computer vision architectures. Our testing data set includes 23 positive and 23 negative ZN-stained slides (from 46 paraffin blocks); the slides were scanned as whole slide images (WSIs). 4 teams of pathologists separately evaluated the WSIs and the original slides; the WSIs were sent for automatic analysis.
WSIs automatic analysis was followed by a report indicating areas more likely to present Mycobacteria. Our architecture showed very good results (0.9020 on ROC curve). The results of final test (machine versus man evaluating WSIs, machine versus man evaluating slides and man evaluating WSIs versus slides) showed accuracy over 90% in all cases.
AI-based software for automated analysis of ZN-stained slides shows reliable results, offering a tremendous economy in pathologist\u2019s time&effort.<\/p>\n
Scurt CV<\/span><\/p>\nProfessor of Pathology in Faculty of Dental Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d Bucharest, senior pathologist and head of the Department of Pathologicy Colentina University Hospital, Bucharest, Romania.
Specializations in the field of dermatopathology (Austria 2012), molecular biology (Italy 2012), digestive pathology (Bucharest 2016, 2015, 2011, 2010, 2009 and 2008), oral pathology (the Netherlands 2005-2006), immunohistochemistry (Bucharest 2004; Greece 2001 ), lymphoid pathology (Greece 2001), pulmonary pathology (Italy 1999), soft tissue pathology (Greece 1997), liver pathology (Greece 1996)
Member of the European Society of Pathology, International Academy of Pathology, International Society of Dermatopathology, Romanian Society of Dermatooncology, Romanian Association of Immunodermatology, European Academy of Dermatovenerology
Author of over 330 scientific papers presented at national and international congresses and symposiums, over 125 articles published in specialized magazines, 23 specialized chapters \/ monographs. H-index (WOS all data bases) 10; citations: 460.
Researcher \/ manager \/ project manager in 16 research projects
Areas of interest: dermatopathology, oral pathology, infectious pathology, soft tissue tumors<\/p>\n
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Contacteaz\u0103 operatorul PDI 2022<\/p>\n
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Operatorul PDI 2022<\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” global_colors_info=”{}”]<\/p>\n
<\/span><\/p>\n<\/span><\/p>\n<\/span><\/p>\nAdresa: Str. A. Panu nr. 13, Iasi<\/span>
Tel.: 0332.40.88.00-05<\/span>
E-mail: contact@pdi.ro<\/a>
<\/span>Website:\u00a0<\/span>www.eventernet.ro<\/a><\/p>\n[\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”Kaushan Script||||||||” text_text_color=”#f0622a” text_font_size=”30px” header_font=”||||||||” background_layout=”dark” disabled=”on” locked=”off” global_colors_info=”{}”]<\/p>\n
Parteneri Media<\/p>\n
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Prof. Dr. Zurac Sabina Universitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti Spitalului Clinic Colentina, Bucure\u0219ti Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, […]<\/p>\n","protected":false},"author":2,"featured_media":11376,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-8671","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lectori"],"_links":{"self":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/comments?post=8671"}],"version-history":[{"count":9,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions"}],"predecessor-version":[{"id":11377,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions\/11377"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media\/11376"}],"wp:attachment":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media?parent=8671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/categories?post=8671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/tags?post=8671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Prof. Dr. Zurac Sabina<\/strong><\/span>
\nUniversitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti
\n Spitalului Clinic Colentina, Bucure\u0219ti
\n<\/strong><\/span><\/h5>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.17.0″ global_colors_info=”{}”]<\/p>\n
Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*autori cu contributie egala<\/span><\/p>\n1 Spitalul Clinic Colentina
2 Universitatea de Medicin\u0103 \u0219i Farmacie Carol Davila Bucuresti
3 Zaya Artificial Intelligence SRL
4 Universitatea Politehnic\u0103 Bucuresti<\/p>\n
Inteligen\u021ba artificial\u0103, tuberculoza, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculoza reprezint\u0103 prima cauz\u0103 de deces prin boal\u0103 infec\u021bioas\u0103. Ideal, diagnosticul TBC se bazeaz\u0103 pe identificarea Mycobacteriilor. Deoarece Mycobacterium tuberculosis este o bacterie de 2-4\/0,2-0,5 microni, c\u0103utarea ei \u00een lamele colorate Ziehl Nielsen (ZN) cu obiectiv microscopic 40X (diametru 0,5 mm) dureaz\u0103 ore; inteligen\u021ba artificial\u0103 poate oferi solu\u021bii viabile \u00een acest domeniu.
Echipa noastr\u0103 include patologi, ingineri de machine learning \u0219i exper\u021bi \u00een IT \u0219i economie. Setul de date de antrenare include 500.000 de casete de delimitare pozitive \u0219i 1.000.000 negative provenite din lamele ZN a 81 de blocuri de parafin\u0103 (21 pozitive, 60 negative); toate lamele au fost scanate folosind scanere manuale \u0219i automate; 7 patologi au notat zone pozitive \u0219i negative. Echipa de machine learning a propus mai multe tehnici de augmentare a imaginii cuplate cu diferite arhitecturi de computer vision. Setul de date de testare include 23 de lame ZN pozitive \u0219i 23 negative (46 de blocuri); lamele au fost scanate integral (whole slide images – WSI). 4 echipe de patologi au evaluat separat WSI-urile \u0219i lamele; WSI-urile au fost \u00eenc\u0103rcate pentru analiz\u0103 automat\u0103.
Analiza automat\u0103 a WSI genereaz\u0103 un raport care indic\u0103 zonele susceptibile de a prezenta micobacterii. Arhitectura noastr\u0103 prezinta rezultate foarte bune (0.9020 pe curba ROC). Rezultatele testului final (ma\u0219in\u0103 versus om care evalueaz\u0103 WSI, ma\u0219in\u0103 versus om care evalueaz\u0103 lame \u0219i om care evalueaz\u0103 WSI versus lame) arat\u0103 acurate\u021be peste 90% \u00een toate cazurile.
Software-ul bazat pe inteligen\u021b\u0103 artificial\u0103 pentru analiza automat\u0103 a lamelor colorate ZN prezint\u0103 rezultate fiabile, oferind o economie extraordinar\u0103 \u00een timpul \u0219i efortul patologului.<\/p>\n
Artificial Intelligence in histopathology: Colentina University Hospital and Zaya Artificial Intelligence experience<\/span>
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*Authors with equal contribution<\/span><\/p>\n1 Colentina University Hospital
2 University of Medicine and Pharmacy Carol Davila Bucharest
3 Zaya Artificial Intelligence SRL
4 University of Polytechnics Bucharest<\/p>\n
Artificial intelligence, tuberculosis, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculosis represents the first cause of death by infectious diseases. Ideally, TB diagnosis relies on Mycobacteria identification. Since Mycobacterium tuberculosis is a 2-4\/0.2-0.5 microns bacterium, its searched in Ziehl Nielsen (ZN) stained-slides with 40X microscopic objective (0.5mm diameter) takes hours; artificial intelligence may offer viable solutions in this field.
Our team includes pathologists from Colentina University Hospital, machine learning engineers from University of Polytechnics Bucharest and experts in IT and economics from Zaya Artificial Intelligence SRL. Our training data set includes 500.000 positive and 1.000.000 negative bounding boxes originating in ZN-stained slides of 81 paraffin blocks (21 positive, 60 negative); all the slides were scanned using manual and automated slide scanners; 7 pathologists annotated positive and negative areas. The machine learning team has proposed several image augmentation techniques coupled with different custom computer vision architectures. Our testing data set includes 23 positive and 23 negative ZN-stained slides (from 46 paraffin blocks); the slides were scanned as whole slide images (WSIs). 4 teams of pathologists separately evaluated the WSIs and the original slides; the WSIs were sent for automatic analysis.
WSIs automatic analysis was followed by a report indicating areas more likely to present Mycobacteria. Our architecture showed very good results (0.9020 on ROC curve). The results of final test (machine versus man evaluating WSIs, machine versus man evaluating slides and man evaluating WSIs versus slides) showed accuracy over 90% in all cases.
AI-based software for automated analysis of ZN-stained slides shows reliable results, offering a tremendous economy in pathologist\u2019s time&effort.<\/p>\n
Scurt CV<\/span><\/p>\nProfessor of Pathology in Faculty of Dental Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d Bucharest, senior pathologist and head of the Department of Pathologicy Colentina University Hospital, Bucharest, Romania.
Specializations in the field of dermatopathology (Austria 2012), molecular biology (Italy 2012), digestive pathology (Bucharest 2016, 2015, 2011, 2010, 2009 and 2008), oral pathology (the Netherlands 2005-2006), immunohistochemistry (Bucharest 2004; Greece 2001 ), lymphoid pathology (Greece 2001), pulmonary pathology (Italy 1999), soft tissue pathology (Greece 1997), liver pathology (Greece 1996)
Member of the European Society of Pathology, International Academy of Pathology, International Society of Dermatopathology, Romanian Society of Dermatooncology, Romanian Association of Immunodermatology, European Academy of Dermatovenerology
Author of over 330 scientific papers presented at national and international congresses and symposiums, over 125 articles published in specialized magazines, 23 specialized chapters \/ monographs. H-index (WOS all data bases) 10; citations: 460.
Researcher \/ manager \/ project manager in 16 research projects
Areas of interest: dermatopathology, oral pathology, infectious pathology, soft tissue tumors<\/p>\n
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Contacteaz\u0103 operatorul PDI 2022<\/p>\n
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Operatorul PDI 2022<\/p>\n
[\/et_pb_text][et_pb_text _builder_version=”4.16″ text_font=”||||||||” global_colors_info=”{}”]<\/p>\n
<\/span><\/p>\n<\/span><\/p>\n<\/span><\/p>\nAdresa: Str. A. Panu nr. 13, Iasi<\/span>
Tel.: 0332.40.88.00-05<\/span>
E-mail: contact@pdi.ro<\/a>
<\/span>Website:\u00a0<\/span>www.eventernet.ro<\/a><\/p>\n[\/et_pb_text][et_pb_text disabled_on=”on|on|on” _builder_version=”4.16″ text_font=”Kaushan Script||||||||” text_text_color=”#f0622a” text_font_size=”30px” header_font=”||||||||” background_layout=”dark” disabled=”on” locked=”off” global_colors_info=”{}”]<\/p>\n
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Prof. Dr. Zurac Sabina Universitatea de Medicin\u0103 \u015fi Farmacie \u201cCarol Davila\u201d Bucure\u015fti Spitalului Clinic Colentina, Bucure\u0219ti Inteligen\u021ba artificial\u0103 \u00een histopatologie: Experien\u021ba Spitalului Clinic Colentina \u0219i Zaya Artificial Intelligence Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, […]<\/p>\n","protected":false},"author":2,"featured_media":11376,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-8671","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lectori"],"_links":{"self":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/comments?post=8671"}],"version-history":[{"count":9,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions"}],"predecessor-version":[{"id":11377,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/posts\/8671\/revisions\/11377"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media\/11376"}],"wp:attachment":[{"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/media?parent=8671"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/categories?post=8671"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/pdi.ro\/2022\/wp-json\/wp\/v2\/tags?post=8671"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*autori cu contributie egala<\/span><\/p>\n
1 Spitalul Clinic Colentina
2 Universitatea de Medicin\u0103 \u0219i Farmacie Carol Davila Bucuresti
3 Zaya Artificial Intelligence SRL
4 Universitatea Politehnic\u0103 Bucuresti<\/p>\n
Inteligen\u021ba artificial\u0103, tuberculoza, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n
Tuberculoza reprezint\u0103 prima cauz\u0103 de deces prin boal\u0103 infec\u021bioas\u0103. Ideal, diagnosticul TBC se bazeaz\u0103 pe identificarea Mycobacteriilor. Deoarece Mycobacterium tuberculosis este o bacterie de 2-4\/0,2-0,5 microni, c\u0103utarea ei \u00een lamele colorate Ziehl Nielsen (ZN) cu obiectiv microscopic 40X (diametru 0,5 mm) dureaz\u0103 ore; inteligen\u021ba artificial\u0103 poate oferi solu\u021bii viabile \u00een acest domeniu.
Echipa noastr\u0103 include patologi, ingineri de machine learning \u0219i exper\u021bi \u00een IT \u0219i economie. Setul de date de antrenare include 500.000 de casete de delimitare pozitive \u0219i 1.000.000 negative provenite din lamele ZN a 81 de blocuri de parafin\u0103 (21 pozitive, 60 negative); toate lamele au fost scanate folosind scanere manuale \u0219i automate; 7 patologi au notat zone pozitive \u0219i negative. Echipa de machine learning a propus mai multe tehnici de augmentare a imaginii cuplate cu diferite arhitecturi de computer vision. Setul de date de testare include 23 de lame ZN pozitive \u0219i 23 negative (46 de blocuri); lamele au fost scanate integral (whole slide images – WSI). 4 echipe de patologi au evaluat separat WSI-urile \u0219i lamele; WSI-urile au fost \u00eenc\u0103rcate pentru analiz\u0103 automat\u0103.
Analiza automat\u0103 a WSI genereaz\u0103 un raport care indic\u0103 zonele susceptibile de a prezenta micobacterii. Arhitectura noastr\u0103 prezinta rezultate foarte bune (0.9020 pe curba ROC). Rezultatele testului final (ma\u0219in\u0103 versus om care evalueaz\u0103 WSI, ma\u0219in\u0103 versus om care evalueaz\u0103 lame \u0219i om care evalueaz\u0103 WSI versus lame) arat\u0103 acurate\u021be peste 90% \u00een toate cazurile.
Software-ul bazat pe inteligen\u021b\u0103 artificial\u0103 pentru analiza automat\u0103 a lamelor colorate ZN prezint\u0103 rezultate fiabile, oferind o economie extraordinar\u0103 \u00een timpul \u0219i efortul patologului.<\/p>\n
Artificial Intelligence in histopathology: Colentina University Hospital and Zaya Artificial Intelligence experience<\/span> 1 Colentina University Hospital Artificial intelligence, tuberculosis, mycobacterium tuberculosis, Ziehl Nielsen<\/p>\n Tuberculosis represents the first cause of death by infectious diseases. Ideally, TB diagnosis relies on Mycobacteria identification. Since Mycobacterium tuberculosis is a 2-4\/0.2-0.5 microns bacterium, its searched in Ziehl Nielsen (ZN) stained-slides with 40X microscopic objective (0.5mm diameter) takes hours; artificial intelligence may offer viable solutions in this field. Scurt CV<\/span><\/p>\n Professor of Pathology in Faculty of Dental Medicine, University of Medicine and Pharmacy \u201cCarol Davila\u201d Bucharest, senior pathologist and head of the Department of Pathologicy Colentina University Hospital, Bucharest, Romania. 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Autori: Sabina Zurac1,2,3*, Cristiana Popp1,3*, M Trascau3,4*, C Mogodici3*, Luciana Nichita1,2,3, Mirela Cioplea1,2,3, T Poncu3, B Ceachi3, Liana Sticlaru1,2,3, Alexandra Cioroianu1,2,3, M Busca1,3, Oana Stefan1, Irina Tudor1, A Voicu3, Daliana Stanescu3, Alexandra Bastian1,2
*Authors with equal contribution<\/span><\/p>\n
2 University of Medicine and Pharmacy Carol Davila Bucharest
3 Zaya Artificial Intelligence SRL
4 University of Polytechnics Bucharest<\/p>\n
Our team includes pathologists from Colentina University Hospital, machine learning engineers from University of Polytechnics Bucharest and experts in IT and economics from Zaya Artificial Intelligence SRL. Our training data set includes 500.000 positive and 1.000.000 negative bounding boxes originating in ZN-stained slides of 81 paraffin blocks (21 positive, 60 negative); all the slides were scanned using manual and automated slide scanners; 7 pathologists annotated positive and negative areas. The machine learning team has proposed several image augmentation techniques coupled with different custom computer vision architectures. Our testing data set includes 23 positive and 23 negative ZN-stained slides (from 46 paraffin blocks); the slides were scanned as whole slide images (WSIs). 4 teams of pathologists separately evaluated the WSIs and the original slides; the WSIs were sent for automatic analysis.
WSIs automatic analysis was followed by a report indicating areas more likely to present Mycobacteria. Our architecture showed very good results (0.9020 on ROC curve). The results of final test (machine versus man evaluating WSIs, machine versus man evaluating slides and man evaluating WSIs versus slides) showed accuracy over 90% in all cases.
AI-based software for automated analysis of ZN-stained slides shows reliable results, offering a tremendous economy in pathologist\u2019s time&effort.<\/p>\n
Specializations in the field of dermatopathology (Austria 2012), molecular biology (Italy 2012), digestive pathology (Bucharest 2016, 2015, 2011, 2010, 2009 and 2008), oral pathology (the Netherlands 2005-2006), immunohistochemistry (Bucharest 2004; Greece 2001 ), lymphoid pathology (Greece 2001), pulmonary pathology (Italy 1999), soft tissue pathology (Greece 1997), liver pathology (Greece 1996)
Member of the European Society of Pathology, International Academy of Pathology, International Society of Dermatopathology, Romanian Society of Dermatooncology, Romanian Association of Immunodermatology, European Academy of Dermatovenerology
Author of over 330 scientific papers presented at national and international congresses and symposiums, over 125 articles published in specialized magazines, 23 specialized chapters \/ monographs. H-index (WOS all data bases) 10; citations: 460.
Researcher \/ manager \/ project manager in 16 research projects
Areas of interest: dermatopathology, oral pathology, infectious pathology, soft tissue tumors<\/p>\n<\/span><\/p>\n
Tel.: 0332.40.88.00-05<\/span>
E-mail: contact@pdi.ro<\/a>
<\/span>Website:\u00a0<\/span>www.eventernet.ro<\/a><\/p>\n