Artificial intelligence model for enhancing the accuracy of transvaginal ultrasound in detecting endometrial cancer and endometrial atypical hyperplasia.
Ilaria CapassoGiuseppe CucinellaDarryl E WrightHiroaki TakahashiLuigi Antonio De VitisAdriana V GregoryBohyun KimEvelyn ReynoldsDiletta FumagalliTommaso OcchialiAngela J FoughtMichaela E McGreeAnnie T PackardPamela I Causa AndrieuFrancesco FanfaniGiovanni ScambiaCarrie L LangstraatAbimbola FamuyideDaniel M BreitkopfAndrea MarianiGretchen E GlaserTimothy L KlinePublished in: International journal of gynecological cancer : official journal of the International Gynecological Cancer Society (2024)
We trained an artificial intelligence based algorithm to differentiate endometrial atypical hyperplasia/cancer from benign conditions on transvaginal ultrasound images in a population of patients with postmenopausal bleeding.
Keyphrases
- artificial intelligence
- endometrial cancer
- deep learning
- machine learning
- big data
- magnetic resonance imaging
- convolutional neural network
- papillary thyroid
- contrast enhanced ultrasound
- ultrasound guided
- atrial fibrillation
- resistance training
- bone mineral density
- squamous cell carcinoma
- body composition
- young adults
- childhood cancer