AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.
Hyunsuk YooSang Hyup LeeChiara Daniela ArruRuhani Doda KheraRamandeep SinghSean SiebertDohoon KimYuna LeeJu Hyun ParkHye Joung EomSubba R DigumarthyMannudeep K KalraPublished in: European radiology (2021)
• Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer. • With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer. • With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.
Keyphrases
- artificial intelligence
- machine learning
- end stage renal disease
- computed tomography
- chronic kidney disease
- magnetic resonance imaging
- image quality
- ejection fraction
- peritoneal dialysis
- prognostic factors
- papillary thyroid
- squamous cell carcinoma
- patient reported
- quantum dots
- lymph node metastasis
- childhood cancer
- structural basis