Analytical Validation of a Deep Neural Network Algorithm for the Detection of Ovarian Cancer.
Gerard ReillyRowan G BullockJessica GreenwoodDaniel R UreErin StewartPierre DavidoffJustin DeGraziaHerbert A FritscheCharles J DuntonNitin BhardwajLesley E NorthropPublished in: JCO clinical cancer informatics (2022)
The balanced performance of MIA3G leads to a high sensitivity and high specificity, a combination that may be clinically useful for providers in evaluating the appropriate management strategy for their patients. Limitations of this work include the largely retrospective nature of the data set and the unequal, albeit random, assignment of histologic subtypes between the training and validation data sets. Future directions may include the addition of new biomarkers or other modalities to strengthen the performance of the algorithm.
Keyphrases
- neural network
- end stage renal disease
- machine learning
- electronic health record
- ejection fraction
- deep learning
- big data
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- patient reported outcomes
- data analysis
- liquid chromatography
- loop mediated isothermal amplification
- current status
- label free
- patient reported
- real time pcr