Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium

Anastasopoulos, Constantin; Yang, Shan; Pradella, Maurice; Akinci D’Antonoli, Tugba; Knecht, Sven; Cyriac, Joshy; Reisert, Marco; Kellner, Elias; Achermann, Rita; Haaf, Philip; Stieltjes, Bram; Sauter, Alexander W.; Bremerich, Jens; Sommer, Gregor; Abdulkadir, Ahmed (2021). Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium. Journal of cardiovascular magnetic resonance, 23(1), p. 133. BioMed Central 10.1186/s12968-021-00791-8

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Background: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process.

Methods and results: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m2 with upper and lower limits of agreement of - 7.5 and 5.8 mL/m2, respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%-from 105 to 34 s, in our in-house clinical setting.

Conclusions: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results.

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