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Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification
| Content Provider | MDPI |
|---|---|
| Author | Sourlos, Nikos Wang, Jingxuan Nagaraj, Yeshaswini van Ooijen, Peter Vliegenthart, Rozemarijn |
| Copyright Year | 2022 |
| Description | Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely. |
| Starting Page | 3867 |
| e-ISSN | 20726694 |
| DOI | 10.3390/cancers14163867 |
| Journal | Cancers |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-10 |
| Access Restriction | Open |
| Subject Keyword | Cancers Pulmonary Nodules Deep Learning Lung Cancer Detection Classification Bias Validation Chest Ct |
| Content Type | Text |
| Resource Type | Article |
