{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:09:42Z","timestamp":1769558982475,"version":"3.49.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Robot. AI"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This paper presents a robotic transesophageal echocardiography (TEE) system that replicates all essential degrees of freedom available in manual TEE procedures. The developed robotic system advances dual-subsystem architectures through enhanced mechanical design and deep learning-based kinematic modeling.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Building upon previous designs that manipulate the TEE probe from both handle and gastroscope tube, our system integrates with a teleoperated UR5 manipulator to accommodate both supine and left lateral decubitus patient positions, addressing the full spectrum of clinical TEE procedures. The system features 6 DOF at the probe handle and 2 DOF at the gastroscope tube. Together, these create optimal gastroscope tube geometry, minimizing cable tension asymmetry and friction-induced nonlinearities inherent in cable-driven mechanisms. The primary contribution is a data-driven kinematic model using recurrent neural networks with LSTM units that overcomes fundamental limitations of analytical approaches for continuum manipulators. Trained on 42,000 synchronized pose-command pairs collected across three gastroscope tube configurations (0\u00b0, 45\u00b0, 90\u00b0 bends), the model effectively captures dead zones, hysteresis, and coupling effects between steering mechanisms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental validation demonstrates strong position tracking across all three gastroscope tube configurations. The model achieves RMSE of 1.267 mm for the 0\u00b0 configuration, 1.209 mm for the 45\u00b0 configuration, and 1.194 mm for the 90\u00b0 configuration. Mean orientation errors are 7.064\u00b0 at 0\u00b0, 8.503\u00b0 at 45\u00b0, and 4.947\u00b0 at the clinically critical 90\u00b0 configuration. The model exhibits coordinate frame independence with only 0.06 mm RMSE difference between original and rotated datasets. This confirms true kinematic learning rather than coordinate-specific patterns. With 1.8 ms inference time, the system achieves real-time performance essential for clinical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This integration of robotic system design with deep learning establishes a foundation for semi-autonomous TEE systems. The developed system can support both diagnostic TEE examinations and TEE-guided structural heart interventions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frobt.2025.1705142","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T11:33:36Z","timestamp":1769513616000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Robotic transesophageal echocardiography: system design and deep learning-based kinematic modeling"],"prefix":"10.3389","volume":"12","author":[{"given":"Seyed MohammadReza","family":"Sajadi","sequence":"first","affiliation":[{"name":"Department of Informatics, University of Oslo","place":["Oslo, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abbas","family":"Tariverdi","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Oslo","place":["Oslo, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Henrik","family":"Brun","sequence":"additional","affiliation":[{"name":"The Intervention Center, Oslo University Hospital","place":["Oslo, Norway"]},{"name":"Department for Pediatric Cardiology, Oslo University Hospital","place":["Oslo, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ole Jakob","family":"Elle","sequence":"additional","affiliation":[{"name":"Department of Informatics, University of Oslo","place":["Oslo, Norway"]},{"name":"The Intervention Center, Oslo University Hospital","place":["Oslo, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kim","family":"Mathiassen","sequence":"additional","affiliation":[{"name":"Department of Technology Systems, University of Oslo","place":["Oslo, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1093\/ehjci\/jeab005","article-title":"The structural heart disease interventional imager rationale, skills and training: a position paper of the european association of cardiovascular imaging","volume":"22","author":"Agricola","year":"2021","journal-title":"Eur. 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