An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis

Römmele, Christoph; Mendel, Robert; Barrett, Caroline; Kiesl, Hans; Rauber, David; Rückert, Tobias; Kraus, Lisa; Heinkele, Jakob; Dhillon, Christine; Grosser, Bianca; Prinz, Friederike; Wanzl, Julia; Fleischmann, Carola; Nagl, Sandra; Schnoy, Elisabeth; Schlottmann, J.; Dellon, Evan S.; Messmann, Helmut; Palm, Christoph; Ebigbo, Alanna

doi:10.1038/s41598-022-14605-z

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…Taken together, these evidence further suggest the pathologic role of mast cells in EoE and have potential implications for antimast cell therapy in EoE. Furthermore, the development of MC-AI is an original usage of artificial intelligence which has the potential to advance the understanding of mast cell associated inflammatory diseases, including eosinophilic esophagitis [28][29][30] .…”

Section: Discussionmentioning

confidence: 71%

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis

Zhang,

Caldwell,

Rochman

et al. 2023

Preprint

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BackgroundEosinophilic esophagitis (EoE) is diagnosed and monitored using esophageal eosinophil levels; however, EoE also exhibits a marked, understudied esophageal mastocytosis.ObjectiveUsing machine learning, we localized and characterized esophageal mast cells to decipher their potential role in disease pathology.MethodsEsophageal biopsy samples (EoE, control) were stained for mast cells by anti-tryptase and imaged using immunofluorescence; high-resolution whole tissue images were digitally assembled. Machine learning software was trained to identify, enumerate, and characterize mast cells, designated Mast Cell-Artificial Intelligence (MC-AI).ResultsMC-AI enumerated cell counts with high accuracy. During active EoE, epithelial mast cells increased and lamina propria (LP) mast cells decreased. In controls and EoE remission patients, papillae had the highest mast cell density and negatively correlated with epithelial mast cell density. Mast cell density in the epithelium and papillae correlated with the degree of epithelial eosinophilic inflammation, basal zone hyperplasia, and LP fibrosis. MC-AI detected greater mast cell degranulation in the epithelium, papillae, and LP in EoE patients compared with control individuals. Mast cells were localized further from the basement membrane in active EoE than EoE remission and controls individuals but were closer than eosinophils to the basement membrane in active EoE.ConclusionUsing MC-AI, we identified a distinct population of homeostatic esophageal papillae mast cells; during active EoE, this population decreases, undergoes degranulation, negatively correlates with epithelial mast cell levels, and significantly correlates with distinct histologic features. Overall, MC-AI provides a means to understand the potential involvement of mast cells in EoE and other disorders.Clinical ImplicationWe have developed a methodology for identifying, enumerating, and characterizing mast cells using artificial intelligence; this has been applied to decipher eosinophilic esophagitis and provides a platform approach for other diseases.Capsule SummaryA machine learning protocol for identifying mast cells, designated Mast Cell–Artificial Intelligence, readily identified spatially distinct and dynamic populations of mast cells in EoE, providing a platform to better understand this cell type in EoE and other diseases.

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Section: Discussionmentioning

confidence: 71%

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis

Zhang,

Caldwell,

Rochman

et al. 2023

Preprint

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“…These results were supported by external validation. Remarkably, the AI model surpassed human endoscopists in predictive accuracy, regardless of the endoscopists' expertise level [85].…”

Section: Eoe Diagnosismentioning

confidence: 95%

The Dual Lens of Endoscopy and Histology in the Diagnosis and Management of Eosinophilic Gastrointestinal Disorders—A Comprehensive Review

Barchi,

Vespa,

Passaretti

et al. 2024

Diagnostics

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Eosinophilic Gastrointestinal Disorders (EGIDs) are a group of conditions characterized by abnormal eosinophil accumulation in the gastrointestinal tract. Among these EGIDs, Eosinophilic Esophagitis (EoE) is the most well documented, while less is known about Eosinophilic Gastritis (EoG), Eosinophilic Enteritis (EoN), and Eosinophilic Colitis (EoC). The role of endoscopy in EGIDs is pivotal, with applications in diagnosis, disease monitoring, and therapeutic intervention. In EoE, the endoscopic reference score (EREFS) has been shown to be accurate in raising diagnostic suspicion and effective in monitoring therapeutic responses. Additionally, endoscopic dilation is the first-line treatment for esophageal strictures. For EoG and EoN, while the literature is more limited, common endoscopic findings include erythema, nodules, and ulcerations. Histology remains the gold standard for diagnosing EGIDs, as it quantifies eosinophilic infiltration. In recent years, there have been significant advancements in the histological understanding of EoE, leading to the development of diagnostic scores and the identification of specific microscopic features associated with the disease. However, for EoG, EoN, and EoC, precise eosinophil count thresholds for diagnosis have not yet been established. This review aims to elucidate the role of endoscopy and histology in the diagnosis and management of the three main EGIDs and to analyze their strengths and limitations, their interconnection, and future research directions.

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“…Einer Augsburger Arbeitsgruppe gelang es, ein KI-System zu entwickeln, das EoE mit hoher Genauigkeit identifizieren kann [5].…”

Section: Merkeunclassified

Künstliche Intelligenz in der Endoskopie

Ebigbo,

Meinikheim,

Schulz

et al. 2023

Gastroenterologie up2date

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An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis

Cited by 10 publications

References 33 publications

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis

Machine Learning Based Identification and Characterization of Mast cells in Eosinophilic Esophagitis

The Dual Lens of Endoscopy and Histology in the Diagnosis and Management of Eosinophilic Gastrointestinal Disorders—A Comprehensive Review

Künstliche Intelligenz in der Endoskopie

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