Deep Learning Pitfall: Impact of Novel Ultrasound Equipment Introduction on Algorithm Performance and the Realities of Domain Adaptation

Abstract

OBJECTIVES: To test deep learning (DL) algorithm performance repercussions by introducing novel ultrasound equipment into a clinical setting. METHODS: Researchers introduced prospectively obtained inferior vena cava (IVC) videos from a similar patient population using novel ultrasound equipment to challenge a previously validated DL algorithm (trained on a common point of care ultrasound [POCUS] machine) to assess IVC collapse. Twenty-one new videos were obtained for each novel ultrasound machine. The videos were analyzed for complete collapse by the algorithm and by 2 blinded POCUS experts. Cohen's kappa was calculated for agreement between the 2 POCUS experts and DL algorithm. Previous testing showed substantial agreement between algorithm and experts with Cohen's kappa of 0.78 (95% CI 0.49-1.0) and 0.66 (95% CI 0.31-1.0) on new patient data using, the same ultrasound equipment. RESULTS: Challenged with higher image quality (IQ) POCUS cart ultrasound videos, algorithm performance declined with kappa values of 0.31 (95% CI 0.19-0.81) and 0.39 (95% CI 0.11-0.89), showing fair agreement. Algorithm performance plummeted on a lower IQ, smartphone device with a kappa value of -0.09 (95% CI -0.95 to 0.76) and 0.09 (95% CI -0.65 to 0.82), respectively, showing less agreement than would be expected by chance. Two POCUS experts had near perfect agreement with a kappa value of 0.88 (95% CI 0.64-1.0) regarding IVC collapse. CONCLUSIONS: Performance of this previously validated DL algorithm worsened when faced with ultrasound studies from 2 novel ultrasound machines. Performance was much worse on images from a lower IQ hand-held device than from a superior cart-based device.

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