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Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks

Gastrointestinal Endoscopy · 2018 · Vol. 89(1) · pp. 25–32

Yoshimasa Horie(Toho University)Toshiyuki Yoshio✉(Japanese Foundation For Cancer Research)Kazuharu AoyamaShoichi Yoshimizu(Japanese Foundation For Cancer Research)Yusuke Horiuchi(Japanese Foundation For Cancer Research)Akiyoshi Ishiyama(Japanese Foundation For Cancer Research)Toshiaki Hirasawa(Japanese Foundation For Cancer Research)Tomohiro Tsuchida(Japanese Foundation For Cancer Research)Tsuyoshi Ozawa(International University of Health and Welfare)Soichiro Ishihara(International University of Health and Welfare)Youichi Kumagai(Saitama Medical University)Mitsuhiro Fujishiro(The University of Tokyo)Iruru Maetani(Toho University)Junko Fujisaki(Japanese Foundation For Cancer Research)Tomohiro Tada(The University of Tokyo)

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Esophageal Cancer Research and Treatment Lung Cancer Diagnosis and Treatment Radiomics and Machine Learning in Medical ImagingMedicineEsophageal cancerCancerConvolutional neural networkDeep learningRadiologyInternal medicineArtificial intelligence

MeSH terms

Deep LearningAdenocarcinomaAgedAged, 80 and overArtificial IntelligenceCarcinoma, Squamous CellDiagnosis, Computer-AssistedEsophageal NeoplasmsFemaleHumansJapanMaleMiddle AgedPredictive Value of TestsRetrospective Studies

Citations

456

FWCI

50.19

field-weighted impact

References

Percentile

100%

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Citations per year

Cited by

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