A research paper for which Kenichiro Suzuki of Dr. Data Science provided statistical analysis support has been accepted and published in the peer-reviewed international journal Surgical Endoscopy (published August 2026).

■ Article Information

Title: Intraoperative real-time recognition of dissectible layers by artificial intelligence is useful in laparoscopic inguinal hernia repair

Authors: Mita K., Mineta S., Takahashi K. et al.

Journal: Surgical Endoscopy

URL：https://link.springer.com/article/10.1007/s00464-026-13241-2

■ Our Contribution

This observational study compared the efficacy and safety of an AI-based surgical support system against conventional techniques in transabdominal preperitoneal inguinal hernia repair (TAPP). Because the two groups differed substantially in sample size, appropriately adjusting for imbalances in baseline characteristics (confounding) was critical to the interpretation of the results.

We therefore supported the statistical analysis throughout the study, including the construction of the propensity score model, the choice of matching method and caliper width, the assessment of covariate balance after matching, and the execution and interpretation of the outcome analyses.

The research team kindly acknowledged our contribution in the published paper as follows:

“The authors would like to thank Mr. Kenichiro Suzuki (dr.data science) for his valuable support in the statistical analysis of this study.” (excerpt from the original text)

■ Statistical Consultation

Dr. Data Science provides end-to-end support, from the study design stage through analysis, interpretation of results, and responses to peer review (including statistical comments raised by reviewers). As in this study, we also handle research requiring advanced methods such as propensity score analysis and survival analysis.

We remain committed to providing careful, thorough support that contributes to our clients’ research achievements. Please feel free to contact us at any time regarding statistical analysis.