TY - JOUR

T1 - Performance and Agreement When Annotating Chest X-ray Text Reports - A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System

AU - Li, Dana

AU - Pehrson, Lea Marie

AU - Bonnevie, Rasmus

AU - Fraccaro, Marco

AU - Thrane, Jakob

AU - Tøttrup, Lea

AU - Lauridsen, Carsten Ammitzbøl

AU - Butt Balaganeshan, Sedrah

AU - Jankovic, Jelena

AU - Andersen, Tobias Thostrup

AU - Mayar, Alyas

AU - Hansen, Kristoffer Lindskov

AU - Carlsen, Jonathan Frederik

AU - Darkner, Sune

AU - Nielsen, Michael Bachmann

PY - 2023

Y1 - 2023

N2 - A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

AB - A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

U2 - 10.3390/diagnostics13061070

DO - 10.3390/diagnostics13061070

M3 - Journal article

C2 - 36980376

VL - 13

JO - Diagnostics

JF - Diagnostics

SN - 2075-4418

IS - 6

ER -