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 -