Loading…
A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy
Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learn...
Saved in:
Published in: | PloS one 2020-11, Vol.15 (11), p.e0242355-e0242355 |
---|---|
Main Authors: | , , , , , , , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313 |
---|---|
cites | cdi_FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313 |
container_end_page | e0242355 |
container_issue | 11 |
container_start_page | e0242355 |
container_title | PloS one |
container_volume | 15 |
creator | Holmström, Oscar Stenman, Sebastian Suutala, Antti Moilanen, Hannu Kücükel, Hakan Ngasala, Billy Mårtensson, Andreas Mhamilawa, Lwidiko Aydin-Schmidt, Berit Lundin, Mikael Diwan, Vinod Linder, Nina Lundin, Johan |
description | Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.
Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.
Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.
Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases. |
doi_str_mv | 10.1371/journal.pone.0242355 |
format | article |
fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2461510961</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A642060776</galeid><doaj_id>oai_doaj_org_article_6beb0355669e47168cd042961ff2da19</doaj_id><sourcerecordid>A642060776</sourcerecordid><originalsourceid>FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313</originalsourceid><addsrcrecordid>eNqNk21r1TAYhosobh79B6KFgSjYY96atl-Ew3wbDCa-7GtI26c9mWlTk3Rz3_3hpjvdWGWClNKQXved5H7yRNFTjNaYZvjNmRltL_V6MD2sEWGEpum9aB8XlCScIHr_1ngveuTcGUIpzTl_GO1RShAuULof_d7EvTkHHdcAQ6xB2l71bVJKB3U8GNX7xDRJJS3EtZJtb5xXVdyB35o6bowNOg-VD5r4s5auM7Uau7iRulKDtGF4ofw2bvRoLLgK-mryaZWXOu5UZY2rzHD5OHoQFA6ezN9V9P3D-2-Hn5Ljk49Hh5vjpMqywid5VjEm8zqHhhRFCZgWNOM0HARoSmnTlFnGU5AN4xmCgqOCsxKDxCivCKOYrqLnO99BGyfmAJ0gjOMUB3oijnZEbeSZGKzqpL0URipxNWFsK6QNCWgQvIQShcw5L4BlmOdVjRgJJk1DahmiX0XFzstdwDCWC7fBmlrM8z_U9AoHArOU8DTFNGhf_1P7Tp1urnYyjoKRvCAk4G_ng41lB3XI2Vuplysu_vRqK1pzLjKeYZbnweDlbGDNzxGcF50K5dJa9mDGXUR5moXjBvTgL_TuIGeqlSEr1TcmrFtNpmLDGUEchVoFan0HFZ4awu0IF7tRYX4heLUQBMbDL9_K0Tlx9PXL_7Mnp0v2xS12C1L7rTN69Mr0bgmyHTjdXWehuQkZIzH15XUaYupLMfdlkD27XaAb0XUj0j8NNzRx</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2461510961</pqid></control><display><type>article</type><title>A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy</title><source>ProQuest - Publicly Available Content Database</source><source>PubMed Central</source><creator>Holmström, Oscar ; Stenman, Sebastian ; Suutala, Antti ; Moilanen, Hannu ; Kücükel, Hakan ; Ngasala, Billy ; Mårtensson, Andreas ; Mhamilawa, Lwidiko ; Aydin-Schmidt, Berit ; Lundin, Mikael ; Diwan, Vinod ; Linder, Nina ; Lundin, Johan</creator><contributor>Carvalho, Luzia Helena</contributor><creatorcontrib>Holmström, Oscar ; Stenman, Sebastian ; Suutala, Antti ; Moilanen, Hannu ; Kücükel, Hakan ; Ngasala, Billy ; Mårtensson, Andreas ; Mhamilawa, Lwidiko ; Aydin-Schmidt, Berit ; Lundin, Mikael ; Diwan, Vinod ; Linder, Nina ; Lundin, Johan ; Carvalho, Luzia Helena</creatorcontrib><description>Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.
Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.
Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.
Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0242355</identifier><identifier>PMID: 33201905</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Algorithms ; Artemisinin ; Asexuality ; Azure Stains ; Biology and Life Sciences ; Blood ; Blood Specimen Collection - methods ; Children & youth ; Childrens health ; Computer aided medical diagnosis ; Correlation ; Correlation analysis ; Deep Learning ; Diagnosis ; Diagnostic systems ; Diagnostic Tests, Routine - instrumentation ; Diagnostic Tests, Routine - methods ; Digital imaging ; Digitization ; Disease ; Entomology ; Fluorescence ; Fluorescence microscopy ; Global health ; Humans ; Infections ; Infectious diseases ; Machine learning ; Malaria ; Malaria - parasitology ; Malaria, Falciparum - diagnosis ; Malaria, Falciparum - parasitology ; Maternal & child health ; Medical diagnosis ; Medicin och hälsovetenskap ; Medicine ; Medicine and Health Sciences ; Methods ; Microscopy ; Microscopy, Fluorescence ; Parasitemia - diagnosis ; Parasites ; Parasitology ; Plasmodium - parasitology ; Plasmodium falciparum ; Plasmodium falciparum - pathogenicity ; Point-of-Care Testing ; Public health ; Research and Analysis Methods ; Vector-borne diseases ; Visual observation ; Visual signals ; Womens health</subject><ispartof>PloS one, 2020-11, Vol.15 (11), p.e0242355-e0242355</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Holmström et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://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.</rights><rights>2020 Holmström et al 2020 Holmström et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313</citedby><cites>FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313</cites><orcidid>0000-0001-9447-4618 ; 0000-0001-5780-0285</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2461510961/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2461510961?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,315,733,786,790,891,25783,27957,27958,37047,37048,44625,53827,53829,75483</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33201905$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-428922$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:145265513$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><contributor>Carvalho, Luzia Helena</contributor><creatorcontrib>Holmström, Oscar</creatorcontrib><creatorcontrib>Stenman, Sebastian</creatorcontrib><creatorcontrib>Suutala, Antti</creatorcontrib><creatorcontrib>Moilanen, Hannu</creatorcontrib><creatorcontrib>Kücükel, Hakan</creatorcontrib><creatorcontrib>Ngasala, Billy</creatorcontrib><creatorcontrib>Mårtensson, Andreas</creatorcontrib><creatorcontrib>Mhamilawa, Lwidiko</creatorcontrib><creatorcontrib>Aydin-Schmidt, Berit</creatorcontrib><creatorcontrib>Lundin, Mikael</creatorcontrib><creatorcontrib>Diwan, Vinod</creatorcontrib><creatorcontrib>Linder, Nina</creatorcontrib><creatorcontrib>Lundin, Johan</creatorcontrib><title>A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.
Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.
Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.
Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Artemisinin</subject><subject>Asexuality</subject><subject>Azure Stains</subject><subject>Biology and Life Sciences</subject><subject>Blood</subject><subject>Blood Specimen Collection - methods</subject><subject>Children & youth</subject><subject>Childrens health</subject><subject>Computer aided medical diagnosis</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Deep Learning</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>Diagnostic Tests, Routine - instrumentation</subject><subject>Diagnostic Tests, Routine - methods</subject><subject>Digital imaging</subject><subject>Digitization</subject><subject>Disease</subject><subject>Entomology</subject><subject>Fluorescence</subject><subject>Fluorescence microscopy</subject><subject>Global health</subject><subject>Humans</subject><subject>Infections</subject><subject>Infectious diseases</subject><subject>Machine learning</subject><subject>Malaria</subject><subject>Malaria - parasitology</subject><subject>Malaria, Falciparum - diagnosis</subject><subject>Malaria, Falciparum - parasitology</subject><subject>Maternal & child health</subject><subject>Medical diagnosis</subject><subject>Medicin och hälsovetenskap</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Microscopy</subject><subject>Microscopy, Fluorescence</subject><subject>Parasitemia - diagnosis</subject><subject>Parasites</subject><subject>Parasitology</subject><subject>Plasmodium - parasitology</subject><subject>Plasmodium falciparum</subject><subject>Plasmodium falciparum - pathogenicity</subject><subject>Point-of-Care Testing</subject><subject>Public health</subject><subject>Research and Analysis Methods</subject><subject>Vector-borne diseases</subject><subject>Visual observation</subject><subject>Visual signals</subject><subject>Womens health</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNk21r1TAYhosobh79B6KFgSjYY96atl-Ew3wbDCa-7GtI26c9mWlTk3Rz3_3hpjvdWGWClNKQXved5H7yRNFTjNaYZvjNmRltL_V6MD2sEWGEpum9aB8XlCScIHr_1ngveuTcGUIpzTl_GO1RShAuULof_d7EvTkHHdcAQ6xB2l71bVJKB3U8GNX7xDRJJS3EtZJtb5xXVdyB35o6bowNOg-VD5r4s5auM7Uau7iRulKDtGF4ofw2bvRoLLgK-mryaZWXOu5UZY2rzHD5OHoQFA6ezN9V9P3D-2-Hn5Ljk49Hh5vjpMqywid5VjEm8zqHhhRFCZgWNOM0HARoSmnTlFnGU5AN4xmCgqOCsxKDxCivCKOYrqLnO99BGyfmAJ0gjOMUB3oijnZEbeSZGKzqpL0URipxNWFsK6QNCWgQvIQShcw5L4BlmOdVjRgJJk1DahmiX0XFzstdwDCWC7fBmlrM8z_U9AoHArOU8DTFNGhf_1P7Tp1urnYyjoKRvCAk4G_ng41lB3XI2Vuplysu_vRqK1pzLjKeYZbnweDlbGDNzxGcF50K5dJa9mDGXUR5moXjBvTgL_TuIGeqlSEr1TcmrFtNpmLDGUEchVoFan0HFZ4awu0IF7tRYX4heLUQBMbDL9_K0Tlx9PXL_7Mnp0v2xS12C1L7rTN69Mr0bgmyHTjdXWehuQkZIzH15XUaYupLMfdlkD27XaAb0XUj0j8NNzRx</recordid><startdate>20201117</startdate><enddate>20201117</enddate><creator>Holmström, Oscar</creator><creator>Stenman, Sebastian</creator><creator>Suutala, Antti</creator><creator>Moilanen, Hannu</creator><creator>Kücükel, Hakan</creator><creator>Ngasala, Billy</creator><creator>Mårtensson, Andreas</creator><creator>Mhamilawa, Lwidiko</creator><creator>Aydin-Schmidt, Berit</creator><creator>Lundin, Mikael</creator><creator>Diwan, Vinod</creator><creator>Linder, Nina</creator><creator>Lundin, Johan</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>ACNBI</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DF2</scope><scope>ZZAVC</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-9447-4618</orcidid><orcidid>https://orcid.org/0000-0001-5780-0285</orcidid></search><sort><creationdate>20201117</creationdate><title>A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy</title><author>Holmström, Oscar ; Stenman, Sebastian ; Suutala, Antti ; Moilanen, Hannu ; Kücükel, Hakan ; Ngasala, Billy ; Mårtensson, Andreas ; Mhamilawa, Lwidiko ; Aydin-Schmidt, Berit ; Lundin, Mikael ; Diwan, Vinod ; Linder, Nina ; Lundin, Johan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Artemisinin</topic><topic>Asexuality</topic><topic>Azure Stains</topic><topic>Biology and Life Sciences</topic><topic>Blood</topic><topic>Blood Specimen Collection - methods</topic><topic>Children & youth</topic><topic>Childrens health</topic><topic>Computer aided medical diagnosis</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>Deep Learning</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>Diagnostic Tests, Routine - instrumentation</topic><topic>Diagnostic Tests, Routine - methods</topic><topic>Digital imaging</topic><topic>Digitization</topic><topic>Disease</topic><topic>Entomology</topic><topic>Fluorescence</topic><topic>Fluorescence microscopy</topic><topic>Global health</topic><topic>Humans</topic><topic>Infections</topic><topic>Infectious diseases</topic><topic>Machine learning</topic><topic>Malaria</topic><topic>Malaria - parasitology</topic><topic>Malaria, Falciparum - diagnosis</topic><topic>Malaria, Falciparum - parasitology</topic><topic>Maternal & child health</topic><topic>Medical diagnosis</topic><topic>Medicin och hälsovetenskap</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Microscopy</topic><topic>Microscopy, Fluorescence</topic><topic>Parasitemia - diagnosis</topic><topic>Parasites</topic><topic>Parasitology</topic><topic>Plasmodium - parasitology</topic><topic>Plasmodium falciparum</topic><topic>Plasmodium falciparum - pathogenicity</topic><topic>Point-of-Care Testing</topic><topic>Public health</topic><topic>Research and Analysis Methods</topic><topic>Vector-borne diseases</topic><topic>Visual observation</topic><topic>Visual signals</topic><topic>Womens health</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Holmström, Oscar</creatorcontrib><creatorcontrib>Stenman, Sebastian</creatorcontrib><creatorcontrib>Suutala, Antti</creatorcontrib><creatorcontrib>Moilanen, Hannu</creatorcontrib><creatorcontrib>Kücükel, Hakan</creatorcontrib><creatorcontrib>Ngasala, Billy</creatorcontrib><creatorcontrib>Mårtensson, Andreas</creatorcontrib><creatorcontrib>Mhamilawa, Lwidiko</creatorcontrib><creatorcontrib>Aydin-Schmidt, Berit</creatorcontrib><creatorcontrib>Lundin, Mikael</creatorcontrib><creatorcontrib>Diwan, Vinod</creatorcontrib><creatorcontrib>Linder, Nina</creatorcontrib><creatorcontrib>Lundin, Johan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Opposing Viewpoints (Gale)</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>ProQuest Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database (Proquest)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Agriculture & Environmental Science Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Agriculture Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>ProQuest Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>ProQuest - Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SWEPUB Uppsala universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Uppsala universitet</collection><collection>SwePub Articles full text</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Holmström, Oscar</au><au>Stenman, Sebastian</au><au>Suutala, Antti</au><au>Moilanen, Hannu</au><au>Kücükel, Hakan</au><au>Ngasala, Billy</au><au>Mårtensson, Andreas</au><au>Mhamilawa, Lwidiko</au><au>Aydin-Schmidt, Berit</au><au>Lundin, Mikael</au><au>Diwan, Vinod</au><au>Linder, Nina</au><au>Lundin, Johan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-11-17</date><risdate>2020</risdate><volume>15</volume><issue>11</issue><spage>e0242355</spage><epage>e0242355</epage><pages>e0242355-e0242355</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><notes>Competing Interests: Johan Lundin and Mikael Lundin are founders and co-owners of Aiforia Technologies Oy, Helsinki, Finland. The Nvidia Corporation donated a GPU via the Academic Grant Application Program, which was used during this study. The prototype instrument used for digitization of the samples is developed and patented (patent number: US20180246306) by the University of Helsinki (Helsinki, Finland). This does not alter our adherence to PLOS ONE policies on sharing data and materials.</notes><abstract>Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.
Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.
Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.
Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33201905</pmid><doi>10.1371/journal.pone.0242355</doi><tpages>e0242355</tpages><orcidid>https://orcid.org/0000-0001-9447-4618</orcidid><orcidid>https://orcid.org/0000-0001-5780-0285</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2020-11, Vol.15 (11), p.e0242355-e0242355 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_2461510961 |
source | ProQuest - Publicly Available Content Database; PubMed Central |
subjects | Adult Algorithms Artemisinin Asexuality Azure Stains Biology and Life Sciences Blood Blood Specimen Collection - methods Children & youth Childrens health Computer aided medical diagnosis Correlation Correlation analysis Deep Learning Diagnosis Diagnostic systems Diagnostic Tests, Routine - instrumentation Diagnostic Tests, Routine - methods Digital imaging Digitization Disease Entomology Fluorescence Fluorescence microscopy Global health Humans Infections Infectious diseases Machine learning Malaria Malaria - parasitology Malaria, Falciparum - diagnosis Malaria, Falciparum - parasitology Maternal & child health Medical diagnosis Medicin och hälsovetenskap Medicine Medicine and Health Sciences Methods Microscopy Microscopy, Fluorescence Parasitemia - diagnosis Parasites Parasitology Plasmodium - parasitology Plasmodium falciparum Plasmodium falciparum - pathogenicity Point-of-Care Testing Public health Research and Analysis Methods Vector-borne diseases Visual observation Visual signals Womens health |
title | A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T08%3A37%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20novel%20deep%20learning-based%20point-of-care%20diagnostic%20method%20for%20detecting%20Plasmodium%20falciparum%20with%20fluorescence%20digital%20microscopy&rft.jtitle=PloS%20one&rft.au=Holmstr%C3%B6m,%20Oscar&rft.date=2020-11-17&rft.volume=15&rft.issue=11&rft.spage=e0242355&rft.epage=e0242355&rft.pages=e0242355-e0242355&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0242355&rft_dat=%3Cgale_plos_%3EA642060776%3C/gale_plos_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c779t-87c44a8d8ef299be1393763190e3533ffb7765eaf4670e960964b1ea108c24313%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2461510961&rft_id=info:pmid/33201905&rft_galeid=A642060776&rfr_iscdi=true |