Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach
Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially du...
Gespeichert in:
| Veröffentlicht in: | PloS one 2019-09, Vol.14 (9), p.e0222030-e0222030 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | e0222030 |
|---|---|
| container_issue | 9 |
| container_start_page | e0222030 |
| container_title | PloS one |
| container_volume | 14 |
| creator | Reismann, Josephine Romualdi, Alessandro Kiss, Natalie Minderjahn, Maximiliane I Kallarackal, Jim Schad, Martina Reismann, Marc |
| description | Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters. |
| doi_str_mv | 10.1371/journal.pone.0222030 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_2297123019</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A600743040</galeid><doaj_id>oai_doaj_org_article_66ed738523214adea8ba476a853211f1</doaj_id><sourcerecordid>A600743040</sourcerecordid><originalsourceid>FETCH-LOGICAL-c758t-edb5c2015fbf8dbfd3f00f4ca396ee9225c8be940be85e9fea23e47296661fdd3</originalsourceid><addsrcrecordid>eNqNk9tu1DAQhiMEomXhDRBEQkJwsYsPiZNwgbQqp0qVKnG6tSb2OOsqa29jp6IPwHvjdLfVLuoF8kWc8Te_Pb89WfackgXlFX134cfBQb_YeIcLwhgjnDzIjmnD2Vykn4d786PsSQgXhJS8FuJxdsRpWfKKNcfZn48WOueDDTk4naseQrDGKojWu9ybfIPaQhysykGNEXPYbNBpq2xMKe11DkOceAt9bl3EvrcdOoX5GuPK6_A-X7q0cIUh2g6iH-bWaZwk0MVJbPCgVk-zRwb6gM9231n28_OnHydf52fnX05PlmdzVZV1nKNuS8UILU1rat0azQ0hplDAG4HYMFaqusWmIC3WJTYGgXEsUp1CCGq05rPs5VZ30_sgdw4GyVhTUcZJ8muWnW4J7eFCbga7huFaerDyJuCHTk4Vqx6lEKgrXpeMM1qARqhbKCoBdZkC1NCk9WG329iuUatU8QD9gejhirMr2fkrKSpBqnI6zJudwOAvx2ShXNugksfg0I83565pUdTpMmfZq3_Q-6vbUR2kAqwzPu2rJlG5FIRUBScFSdTiHioNjWur0nMzNsUPEt4eJCQm4u_YwRiCPP3-7f_Z81-H7Os9doXQx1Xw_Ti9zXAIFltQDT6EAc2dyZTIqVtu3ZBTt8hdt6S0F_sXdJd02x78L4vsEl4</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2297123019</pqid></control><display><type>article</type><title>Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Public Library of Science (PLoS) Journals Open Access</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Reismann, Josephine ; Romualdi, Alessandro ; Kiss, Natalie ; Minderjahn, Maximiliane I ; Kallarackal, Jim ; Schad, Martina ; Reismann, Marc</creator><contributor>Baltzer, Pascal A. T.</contributor><creatorcontrib>Reismann, Josephine ; Romualdi, Alessandro ; Kiss, Natalie ; Minderjahn, Maximiliane I ; Kallarackal, Jim ; Schad, Martina ; Reismann, Marc ; Baltzer, Pascal A. T.</creatorcontrib><description>Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0222030</identifier><identifier>PMID: 31553729</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adolescents ; Age ; Algorithms ; Appendectomy ; Appendicitis ; Appendicitis - classification ; Appendicitis - diagnosis ; Appendicitis - surgery ; Appendix ; Appendix - diagnostic imaging ; Artificial Intelligence ; Biology and Life Sciences ; Biomarkers ; Biomarkers - blood ; Blood ; Blood Cell Count ; C-reactive protein ; C-Reactive Protein - metabolism ; Care and treatment ; Child ; Child, Preschool ; Childhood ; Children ; Classification ; Clinical decision making ; Decision making ; Diagnosis ; Diagnosis, Computer-Assisted - methods ; Diagnostic systems ; Female ; Gangrene ; Germany ; Granulocytes ; Histochemistry ; Histopathology ; Humans ; Infant ; Infant, Newborn ; Inflammation ; Laboratories ; Learning algorithms ; Machine Learning ; Male ; Medical diagnosis ; Medicine and Health Sciences ; Methods ; Neural networks ; Neutrophils ; Parameters ; Patients ; Pediatric research ; Pediatrics ; Research and Analysis Methods ; Retrospective Studies ; Risk factors ; ROC Curve ; Sensitivity ; Statistical analysis ; Surgery ; Systematic review ; Ultrasonic imaging ; Ultrasonic testing ; Ultrasonography ; Ultrasound ; Youth</subject><ispartof>PloS one, 2019-09, Vol.14 (9), p.e0222030-e0222030</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Reismann 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>2019 Reismann et al 2019 Reismann et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c758t-edb5c2015fbf8dbfd3f00f4ca396ee9225c8be940be85e9fea23e47296661fdd3</citedby><cites>FETCH-LOGICAL-c758t-edb5c2015fbf8dbfd3f00f4ca396ee9225c8be940be85e9fea23e47296661fdd3</cites><orcidid>0000-0002-1747-0493 ; 0000-0001-7426-1578</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760759/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760759/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31553729$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Baltzer, Pascal A. T.</contributor><creatorcontrib>Reismann, Josephine</creatorcontrib><creatorcontrib>Romualdi, Alessandro</creatorcontrib><creatorcontrib>Kiss, Natalie</creatorcontrib><creatorcontrib>Minderjahn, Maximiliane I</creatorcontrib><creatorcontrib>Kallarackal, Jim</creatorcontrib><creatorcontrib>Schad, Martina</creatorcontrib><creatorcontrib>Reismann, Marc</creatorcontrib><title>Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.</description><subject>Adolescent</subject><subject>Adolescents</subject><subject>Age</subject><subject>Algorithms</subject><subject>Appendectomy</subject><subject>Appendicitis</subject><subject>Appendicitis - classification</subject><subject>Appendicitis - diagnosis</subject><subject>Appendicitis - surgery</subject><subject>Appendix</subject><subject>Appendix - diagnostic imaging</subject><subject>Artificial Intelligence</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Biomarkers - blood</subject><subject>Blood</subject><subject>Blood Cell Count</subject><subject>C-reactive protein</subject><subject>C-Reactive Protein - metabolism</subject><subject>Care and treatment</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Childhood</subject><subject>Children</subject><subject>Classification</subject><subject>Clinical decision making</subject><subject>Decision making</subject><subject>Diagnosis</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Diagnostic systems</subject><subject>Female</subject><subject>Gangrene</subject><subject>Germany</subject><subject>Granulocytes</subject><subject>Histochemistry</subject><subject>Histopathology</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Inflammation</subject><subject>Laboratories</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Neutrophils</subject><subject>Parameters</subject><subject>Patients</subject><subject>Pediatric research</subject><subject>Pediatrics</subject><subject>Research and Analysis Methods</subject><subject>Retrospective Studies</subject><subject>Risk factors</subject><subject>ROC Curve</subject><subject>Sensitivity</subject><subject>Statistical analysis</subject><subject>Surgery</subject><subject>Systematic review</subject><subject>Ultrasonic imaging</subject><subject>Ultrasonic testing</subject><subject>Ultrasonography</subject><subject>Ultrasound</subject><subject>Youth</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9tu1DAQhiMEomXhDRBEQkJwsYsPiZNwgbQqp0qVKnG6tSb2OOsqa29jp6IPwHvjdLfVLuoF8kWc8Te_Pb89WfackgXlFX134cfBQb_YeIcLwhgjnDzIjmnD2Vykn4d786PsSQgXhJS8FuJxdsRpWfKKNcfZn48WOueDDTk4naseQrDGKojWu9ybfIPaQhysykGNEXPYbNBpq2xMKe11DkOceAt9bl3EvrcdOoX5GuPK6_A-X7q0cIUh2g6iH-bWaZwk0MVJbPCgVk-zRwb6gM9231n28_OnHydf52fnX05PlmdzVZV1nKNuS8UILU1rat0azQ0hplDAG4HYMFaqusWmIC3WJTYGgXEsUp1CCGq05rPs5VZ30_sgdw4GyVhTUcZJ8muWnW4J7eFCbga7huFaerDyJuCHTk4Vqx6lEKgrXpeMM1qARqhbKCoBdZkC1NCk9WG329iuUatU8QD9gejhirMr2fkrKSpBqnI6zJudwOAvx2ShXNugksfg0I83565pUdTpMmfZq3_Q-6vbUR2kAqwzPu2rJlG5FIRUBScFSdTiHioNjWur0nMzNsUPEt4eJCQm4u_YwRiCPP3-7f_Z81-H7Os9doXQx1Xw_Ti9zXAIFltQDT6EAc2dyZTIqVtu3ZBTt8hdt6S0F_sXdJd02x78L4vsEl4</recordid><startdate>20190925</startdate><enddate>20190925</enddate><creator>Reismann, Josephine</creator><creator>Romualdi, Alessandro</creator><creator>Kiss, Natalie</creator><creator>Minderjahn, Maximiliane I</creator><creator>Kallarackal, Jim</creator><creator>Schad, Martina</creator><creator>Reismann, Marc</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>DOA</scope><orcidid>https://orcid.org/0000-0002-1747-0493</orcidid><orcidid>https://orcid.org/0000-0001-7426-1578</orcidid></search><sort><creationdate>20190925</creationdate><title>Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach</title><author>Reismann, Josephine ; Romualdi, Alessandro ; Kiss, Natalie ; Minderjahn, Maximiliane I ; Kallarackal, Jim ; Schad, Martina ; Reismann, Marc</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c758t-edb5c2015fbf8dbfd3f00f4ca396ee9225c8be940be85e9fea23e47296661fdd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Adolescents</topic><topic>Age</topic><topic>Algorithms</topic><topic>Appendectomy</topic><topic>Appendicitis</topic><topic>Appendicitis - classification</topic><topic>Appendicitis - diagnosis</topic><topic>Appendicitis - surgery</topic><topic>Appendix</topic><topic>Appendix - diagnostic imaging</topic><topic>Artificial Intelligence</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Biomarkers - blood</topic><topic>Blood</topic><topic>Blood Cell Count</topic><topic>C-reactive protein</topic><topic>C-Reactive Protein - metabolism</topic><topic>Care and treatment</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Childhood</topic><topic>Children</topic><topic>Classification</topic><topic>Clinical decision making</topic><topic>Decision making</topic><topic>Diagnosis</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Diagnostic systems</topic><topic>Female</topic><topic>Gangrene</topic><topic>Germany</topic><topic>Granulocytes</topic><topic>Histochemistry</topic><topic>Histopathology</topic><topic>Humans</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Inflammation</topic><topic>Laboratories</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Neutrophils</topic><topic>Parameters</topic><topic>Patients</topic><topic>Pediatric research</topic><topic>Pediatrics</topic><topic>Research and Analysis Methods</topic><topic>Retrospective Studies</topic><topic>Risk factors</topic><topic>ROC Curve</topic><topic>Sensitivity</topic><topic>Statistical analysis</topic><topic>Surgery</topic><topic>Systematic review</topic><topic>Ultrasonic imaging</topic><topic>Ultrasonic testing</topic><topic>Ultrasonography</topic><topic>Ultrasound</topic><topic>Youth</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Reismann, Josephine</creatorcontrib><creatorcontrib>Romualdi, Alessandro</creatorcontrib><creatorcontrib>Kiss, Natalie</creatorcontrib><creatorcontrib>Minderjahn, Maximiliane I</creatorcontrib><creatorcontrib>Kallarackal, Jim</creatorcontrib><creatorcontrib>Schad, Martina</creatorcontrib><creatorcontrib>Reismann, Marc</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</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>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>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</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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>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</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural 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>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>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>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>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Reismann, Josephine</au><au>Romualdi, Alessandro</au><au>Kiss, Natalie</au><au>Minderjahn, Maximiliane I</au><au>Kallarackal, Jim</au><au>Schad, Martina</au><au>Reismann, Marc</au><au>Baltzer, Pascal A. T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-09-25</date><risdate>2019</risdate><volume>14</volume><issue>9</issue><spage>e0222030</spage><epage>e0222030</epage><pages>e0222030-e0222030</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acute appendicitis remains challenging, especially due to the frequently unspecific clinical picture. Inflammatory blood markers and imaging methods like ultrasound are limited as they have to be interpreted by experts and still do not offer sufficient diagnostic certainty. This study presents a method for automatic diagnosis of appendicitis as well as the differentiation between complicated and uncomplicated inflammation using values/parameters which are routinely and unbiasedly obtained for each patient with suspected appendicitis. We analyzed full blood counts, c-reactive protein (CRP) and appendiceal diameters in ultrasound investigations corresponding to children and adolescents aged 0-17 years from a hospital based population in Berlin, Germany. A total of 590 patients (473 patients with appendicitis in histopathology and 117 with negative histopathological findings) were analyzed retrospectively with modern algorithms from machine learning (ML) and artificial intelligence (AI). The discovery of informative parameters (biomarker signatures) and training of the classification model were done with a maximum of 35% of the patients. The remaining minimum 65% of patients were used for validation. At clinical relevant cut-off points the accuracy of the biomarker signature for diagnosis of appendicitis was 90% (93% sensitivity, 67% specificity), while the accuracy to correctly identify complicated inflammation was 51% (95% sensitivity, 33% specificity) on validation data. Such a test would be capable to prevent two out of three patients without appendicitis from useless surgery as well as one out of three patients with uncomplicated appendicitis. The presented method has the potential to change today's therapeutic approach for appendicitis and demonstrates the capability of algorithms from AI and ML to significantly improve diagnostics even based on routine diagnostic parameters.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31553729</pmid><doi>10.1371/journal.pone.0222030</doi><tpages>e0222030</tpages><orcidid>https://orcid.org/0000-0002-1747-0493</orcidid><orcidid>https://orcid.org/0000-0001-7426-1578</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-09, Vol.14 (9), p.e0222030-e0222030 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2297123019 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Public Library of Science (PLoS) Journals Open Access; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adolescent Adolescents Age Algorithms Appendectomy Appendicitis Appendicitis - classification Appendicitis - diagnosis Appendicitis - surgery Appendix Appendix - diagnostic imaging Artificial Intelligence Biology and Life Sciences Biomarkers Biomarkers - blood Blood Blood Cell Count C-reactive protein C-Reactive Protein - metabolism Care and treatment Child Child, Preschool Childhood Children Classification Clinical decision making Decision making Diagnosis Diagnosis, Computer-Assisted - methods Diagnostic systems Female Gangrene Germany Granulocytes Histochemistry Histopathology Humans Infant Infant, Newborn Inflammation Laboratories Learning algorithms Machine Learning Male Medical diagnosis Medicine and Health Sciences Methods Neural networks Neutrophils Parameters Patients Pediatric research Pediatrics Research and Analysis Methods Retrospective Studies Risk factors ROC Curve Sensitivity Statistical analysis Surgery Systematic review Ultrasonic imaging Ultrasonic testing Ultrasonography Ultrasound Youth |
| title | Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-19T02%3A13%3A17IST&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=Diagnosis%20and%20classification%20of%20pediatric%20acute%20appendicitis%20by%20artificial%20intelligence%20methods:%20An%20investigator-independent%20approach&rft.jtitle=PloS%20one&rft.au=Reismann,%20Josephine&rft.date=2019-09-25&rft.volume=14&rft.issue=9&rft.spage=e0222030&rft.epage=e0222030&rft.pages=e0222030-e0222030&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0222030&rft_dat=%3Cgale_plos_%3EA600743040%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2297123019&rft_id=info:pmid/31553729&rft_galeid=A600743040&rft_doaj_id=oai_doaj_org_article_66ed738523214adea8ba476a853211f1&rfr_iscdi=true |