TY - JOUR
T1 - Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows
T2 - A Systematic Review
AU - Sheng, Kaining
AU - Offersen, Cecilie Morck
AU - Middleton, Jon
AU - Carlsen, Jonathan Frederik
AU - Truelsen, Thomas Clement
AU - Pai, Akshay
AU - Johansen, Jacob
AU - Nielsen, Michael Bachmann
PY - 2022/8
Y1 - 2022/8
N2 - We conducted a systematic review of the current status of machine learning (ML) algorithms' ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study's design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.
AB - We conducted a systematic review of the current status of machine learning (ML) algorithms' ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study's design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.
KW - artificial intelligence
KW - machine learning
KW - brain MRI
KW - brain diseases
KW - workflow
KW - ARTIFICIAL-INTELLIGENCE
KW - APPROPRIATENESS
U2 - 10.3390/diagnostics12081878
DO - 10.3390/diagnostics12081878
M3 - Review
C2 - 36010228
VL - 12
JO - Diagnostics
JF - Diagnostics
SN - 0336-3449
IS - 8
M1 - 1878
ER -