Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.
Simple Summary: Artificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There...
| Published in: | Cancers Vol. 14; no. 16; pp. 3867 - 3882 |
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| Main Authors: | , , , , |
| Format: | diagnostic images review tables/charts Journal Article |
| Published: |
MDPI
Aug2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158750435&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158750435 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Aug2022 vid: 14 iid: 16 pid: 97109 pub: MDPI artinfo: ui: 158750435 158750435 158750435 10.3390/cancers14163867 158750435 ppf: 3867 ppct: 15 formats: tig: atl: Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification. aug: au: Sourlos, Nikos Wang, Jingxuan Nagaraj, Yeshaswini van Ooijen, Peter Vliegenthart, Rozemarijn affil: Department of Radiology, University Medical Center of Groningen, 9713 GZ Groningen, The Netherlands sug: subj: Solitary Pulmonary Nodule Diagnosis Solitary Pulmonary Nodule Classification Solitary Pulmonary Nodule Radiography Tomography, X-Ray Computed Deep Learning Methods Artificial Intelligence Utilization Algorithms Utilization Bias (Research) Early Detection of Cancer ab: Simple Summary: Artificial Intelligence (AI) algorithms can assist clinicians in their daily tasks by automatically detecting and/or classifying nodules in chest CT scans. Bias of such algorithms is one of the reasons why implementation of them in clinical practice is still not widely adopted. There is no published review on the bias that these algorithms may contain. This review aims to present different types of bias in such algorithms and present possible ways to mitigate them. Only then it would be possible to ensure that these algorithms work as intended under many different clinical settings. 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. pubtype: Academic Journal doctype: diagnostic images review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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