Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions.
Objectives: This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue.Methods: Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tiss...
| Publicado en: | American Journal of Clinical Pathology Vol. 155; no. 4; pp. 527 - 537 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149338766&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149338766 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00029173 2RW jtl: American Journal of Clinical Pathology issn: 00029173 maglogo: N pubinfo: dt: Apr2021 vid: 155 iid: 4 pid: 10398 pub: Oxford University Press / USA artinfo: ui: 149338766 149338766 NLM33118594 149338766 10.1093/ajcp/aqaa151 NLM33118594 149338766 ppf: 527 ppct: 10 formats: tig: atl: Artificial Intelligence Improves the Accuracy in Histologic Classification of Breast Lesions. aug: au: Polónia, António Campelos, Sofia Ribeiro, Ana Aymore, Ierece Pinto, Daniel Biskup-Fruzynska, Magdalena Veiga, Ricardo Santana Canas-Marques, Rita Aresta, Guilherme Araújo, Teresa Campilho, Aurélio Kwok, Scotty Aguiar, Paulo Eloy, Catarina affil: Department of Pathology, Ipatimup Diagnostics, Institute of Molecular Pathology and Immunology, University of Porto , Porto, Portugal sug: subj: Diagnosis, Computer Assisted Methods Breast Neoplasms Classification Artificial Intelligence Breast Neoplasms Pathology Image Interpretation, Computer Assisted Methods Female Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Female ab: Objectives: This study evaluated the usefulness of artificial intelligence (AI) algorithms as tools in improving the accuracy of histologic classification of breast tissue.Methods: Overall, 100 microscopic photographs (test A) and 152 regions of interest in whole-slide images (test B) of breast tissue were classified into 4 classes: normal, benign, carcinoma in situ (CIS), and invasive carcinoma. The accuracy of 4 pathologists and 3 pathology residents were evaluated without and with the assistance of algorithms.Results: In test A, algorithm A had accuracy of 0.87, with the lowest accuracy in the benign class (0.72). The observers had average accuracy of 0.80, and most clinically relevant discordances occurred in distinguishing benign from CIS (7.1% of classifications). With the assistance of algorithm A, the observers significantly increased their average accuracy to 0.88. In test B, algorithm B had accuracy of 0.49, with the lowest accuracy in the CIS class (0.06). The observers had average accuracy of 0.86, and most clinically relevant discordances occurred in distinguishing benign from CIS (6.3% of classifications). With the assistance of algorithm B, the observers maintained their average accuracy.Conclusions: AI tools can increase the classification accuracy of pathologists in the setting of breast lesions. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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