  $\textbf{Background and aims}$: Artificial Intelligence (AI) Computer-Aided
Detection (CADe) is commonly used for polyp detection, but data seen in
clinical settings can differ from model training. Few studies evaluate how well
CADe detectors perform on colonoscopies from countries not seen during
training, and none are able to evaluate performance without collecting
expensive and time-intensive labels.
  $\textbf{Methods}$: We trained a CADe polyp detector on Israeli colonoscopy
videos (5004 videos, 1106 hours) and evaluated on Japanese videos (354 videos,
128 hours) by measuring the True Positive Rate (TPR) versus false alarms per
minute (FAPM). We introduce a colonoscopy dissimilarity measure called "MAsked
mediCal Embedding Distance" (MACE) to quantify differences between
colonoscopies, without labels. We evaluated CADe on all Japan videos and on
those with the highest MACE.
  $\textbf{Results}$: MACE correctly quantifies that narrow-band imaging (NBI)
and chromoendoscopy (CE) frames are less similar to Israel data than Japan
whitelight (bootstrapped z-test, |z| > 690, p < $10^{-8}$ for both). Despite
differences in the data, CADe performance on Japan colonoscopies was
non-inferior to Israel ones without additional training (TPR at 0.5 FAPM: 0.957
and 0.972 for Israel and Japan; TPR at 1.0 FAPM: 0.972 and 0.989 for Israel and
Japan; superiority test t > 45.2, p < $10^{-8}$). Despite not being trained on
NBI or CE, TPR on those subsets were non-inferior to Japan overall
(non-inferiority test t > 47.3, p < $10^{-8}$, $\delta$ = 1.5% for both).
  $\textbf{Conclusion}$: Differences that prevent CADe detectors from
performing well in non-medical settings do not degrade the performance of our
AI CADe polyp detector when applied to data from a new country. MACE can help
medical AI models internationalize by identifying the most "dissimilar" data on
which to evaluate models.
