The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease

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dc.authorid0000-0002-6333-8856
dc.authorid0000-0001-5943-7440
dc.authorid0000-0003-0908-9630
dc.contributor.authorDemir, Derya
dc.contributor.authorOzyoruk, Kutsev Bengisu
dc.contributor.authorDurusoy, Yasin
dc.contributor.authorCinar, Ezgi
dc.contributor.authorSerin, Gurdeniz
dc.contributor.authorBasak, Kayhan
dc.contributor.authorTuran, Mehmet
dc.contributor.authorYakut, Basak Doganavsargil
dc.date.accessioned2025-02-18T07:15:47Z
dc.date.available2025-02-18T07:15:47Z
dc.date.issued2024
dc.departmentEge Üniversitesi, Tıp Fakültesi, Cerrahi Bilimler Bölümü, Tıbbi Patoloji Ana Bilim Dalı
dc.description.abstractHirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach that significantly enhances the detection of ganglion cells in frozen sections. The data set comprises 366 frozen and 302 formalin-fixed-paraffinembedded hematoxylin and eosin-stained slides obtained from 164 patients from 3 centers. The ganglion cells were annotated on the whole-slide images (WSIs) using bounding boxes. Tissue regions within WSIs were segmented and split into patches of 2000 x 2000 pixels. A deep learning pipeline utilizing ResNet-50 model for feature extraction and gradient-weighted class activation mapping algorithm to generate heatmaps for ganglion cell localization was employed. The binary classification performance of the model was evaluated on independent test cohorts. In the multireader study, 10 pathologists assessed 50 frozen WSIs, with 25 slides containing ganglion cells, and 25 slides without. In the first phase of the study, pathologists evaluated the slides as a routine practice. After a 2-week washout period, pathologists re-evaluated the same WSIs along with the 4 patches with the highest probability of containing ganglion cells. The proposed deep learning approach achieved an accuracy of 91.3%, 92.8%, and 90.1% in detecting ganglion cells within WSIs in the test data set obtained from centers. In the reader study, on average, the pathologists' diagnostic accuracy increased from 77% to 85.8% with the model's heatmap support, whereas the diagnosis time decreased from an average of 139.7 to 70.5 seconds. Notably, when applied in real-world settings with a group of pathologists, our model's integration brought about substantial improvement in diagnosis precision and reduced the time required for diagnoses by half. This notable advance in artificial intelligence-driven diagnostics not only sets a new standard for surgical decision making in Hirschsprung disease but also creates opportunities for its wider implementation in various clinical settings, highlighting its pivotal role in enhancing the efficacy and accuracy of frozen sections analyses. (c) 2024 United States & Canadian Academy of Pathology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
dc.identifier.citationDemir, D., Ozyoruk, K. B., Durusoy, Y., Cinar, E., Serin, G., Basak, K., Kose, E. C., Ergin, M., Sezak, M., Keles, G. E., Dervisoglu, S., Yakut, B. D., Ertas, Y. N., Alaqad, F., & Turan, M. (2025). The future of surgical diagnostics: Artificial intelligence-enhanced detection of ganglion cells for hirschsprung disease. Laboratory Investigation, 105(2), 102189.
dc.identifier.doi10.1016/j.labinv.2024.102189
dc.identifier.endpage-
dc.identifier.issn0023-6837
dc.identifier.issue2
dc.identifier.pmid39577743
dc.identifier.scopus2-s2.0-85212324038
dc.identifier.scopusqualityQ2
dc.identifier.startpage-
dc.identifier.urihttps://doi.org/10.1016/j.labinv.2024.102189
dc.identifier.urihttps://hdl.handle.net/11454/116073
dc.identifier.volume105
dc.identifier.wosWOS:001391208700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorDemir, Derya
dc.institutionauthorSezak, Murat
dc.institutionauthorYakut, Basak Doganavsargil
dc.institutionauthorid0000-0002-6333-8856
dc.institutionauthorid0000-0002-0457-4832
dc.institutionauthorid0000-0002-4738-4350
dc.language.isoen
dc.publisherElsevier Sci Inc
dc.relation.ispartofLaboratory Investigation
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectartificial intelligence
dc.subjectfrozen
dc.subjectHirschsprung disease
dc.subjectsurgical pathology
dc.titleThe Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease
dc.typeArticle

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