{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:19:42Z","timestamp":1763565582266,"version":"3.45.0"},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T00:00:00Z","timestamp":1763337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"project \u201cRomanian Hub for Artificial Intelligence -HRIA\u201d, Smart Growth, Digitization and Financial Instruments Program","award":["334906"],"award-info":[{"award-number":["334906"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>(1) Background: Most robotic MIS platforms lack native haptic feedback, leaving surgeons to infer tissue loads from vision alone\u2014an especially risky limitation in esophageal procedures. (2) Methods: We develop a sensorless, image-only force-estimation pipeline that maps endoscopic video to tool\u2013tissue forces using a lightweight EfficientNetV2B0 CNN. The model is trained on 9691 labeled frames from in vitro esophageal experiments and validated against an FT300 load cell. For intraoperative feasibility, the system is deployed as a plug-in on PARA-SILSROB, consuming the existing laparoscope feed and driving a commercial haptic device. The runtime processes every 10th frame of a 60 FPS stream (\u22486 Hz updates) with ~15\u201320 ms per-prediction latency. (3) Results: On held-out tests, the model achieves MAE = 0.017 N and MSE = 0.0004 N2, outperforming a recurrent CNN baseline while maintaining real-time performance on commodity hardware. Integrated evaluations confirm stable operation at the deployed update rate and low latency compatible with closed-loop haptics. (4) Conclusions: By avoiding distal force sensors and preserving sterile workflow, the approach is readily translatable and retrofit-friendly for current robotic platforms. The results support the practical feasibility of real-time, sensorless force feedback for robotic esophagectomy and related MIS tasks, with potential to reduce tissue trauma and enhance operative safety.<\/jats:p>","DOI":"10.3390\/info16110993","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:03:15Z","timestamp":1763564595000},"page":"993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An AI-Based Sensorless Force Feedback in Robot-Assisted Minimally Invasive Surgery"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7014-9431","authenticated-orcid":false,"given":"Doina","family":"Pisla","sequence":"first","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Technical Sciences Academy of Romania, B-dul Dacia, 26, 030167 Bucharest, Romania"}]},{"given":"Nadim Al","family":"Hajjar","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Department of Surgery, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"}]},{"given":"Gabriela","family":"Rus","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}]},{"given":"Calin","family":"Popa","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Department of Surgery, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4427-6231","authenticated-orcid":false,"given":"Bogdan","family":"Gherman","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0126-6428","authenticated-orcid":false,"given":"Andra","family":"Ciocan","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Department of Surgery, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"}]},{"given":"Andrei","family":"Cailean","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}]},{"given":"Corina","family":"Radu","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Department of Internal Medicine, \u201cIuliu Hatieganu\u201d University of Medicine and Pharmacy, 400347 Cluj-Napoca, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7847-6162","authenticated-orcid":false,"given":"Damien","family":"Chablat","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"},{"name":"Laboratory of Numerical Sciences in Nantes, Joint Research Unit 6004, Centre National de la Recherche Scientifique (CNRS), \u00c9cole Centrale Nantes, Nantes Universit\u00e9, F-44000 Nantes, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2822-9790","authenticated-orcid":false,"given":"Calin","family":"Vaida","sequence":"additional","affiliation":[{"name":"CESTER\u2014Research Center for Industrial Robots Simulation and Testing, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9853-7102","authenticated-orcid":false,"given":"Anca-Elena","family":"Iordan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,17]]},"reference":[{"key":"ref_1","first-page":"e29179","article-title":"Robotic Surgery: A Narrative Review","volume":"14","author":"Bramhe","year":"2022","journal-title":"Cureus"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1007\/s00464-022-09073-5","article-title":"Impact of type of minimally invasive approach on open conversions across ten common procedures in different specialties","volume":"36","author":"Shah","year":"2022","journal-title":"Surg. Endosc."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pisla, D., Plitea, N., Gherman, B., Pisla, A., and Vaida, C. (2009, January 6\u20138). Kinematical Analysis and Design of a New Surgical Parallel Robot. Proceedings of the 5th International Workshop on Computational Kinematics, Duisburg, Germany.","DOI":"10.1007\/978-3-642-01947-0_34"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2100011","DOI":"10.1002\/aisy.202100011","article-title":"Intelligent Soft Surgical Robots for Next-Generation Minimally Invasive Surgery","volume":"3","author":"Zhu","year":"2021","journal-title":"Adv. Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/03091902.2020.1772391","article-title":"A review of haptic feedback in tele-operated robotic surgery","volume":"44","year":"2020","journal-title":"J. Med. Eng. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Xia, X., Sun, J., Liang, B., Ma, Y., and Fu, Y. (2024). Master-Slave Teleoperation Robot System Design. Chinese Intelligent Systems, Springer.","DOI":"10.1007\/978-981-97-8650-3_12"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"doaa109","DOI":"10.1093\/dote\/doaa109","article-title":"Specific complications and limitations of robotic esophagectomy","volume":"33","author":"Abbas","year":"2020","journal-title":"Dis. Esophagus"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alemzadeh, H., Raman, J., Leveson, N., Kalbarczyk, Z., and Iyer, R.K. (2016). Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0151470"},{"key":"ref_9","first-page":"201","article-title":"Innovative development of surgical parallel robots","volume":"4","author":"Plitea","year":"2007","journal-title":"Acta Electron. Mediamira Sci. Cluj-Napoca"},{"key":"ref_10","first-page":"361","article-title":"Development of a learning management system for knowledge transfer in engineering","volume":"64","author":"Pisla","year":"2021","journal-title":"Acta Tech. Napoc.-Ser. Appl. Math. Mech. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1109\/TMRB.2024.3407238","article-title":"Development of force sensing techniques for robot-assisted laparoscopic surgery: A review","volume":"6","author":"Hao","year":"2024","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, H., Zhang, B., Mei, X., and Song, Q. (2020). Realization of Force Detection and Feedback Control for Slave Manipulator of Master\/Slave Surgical Robot. Sensors, 21.","DOI":"10.3390\/s21227489"},{"key":"ref_13","first-page":"7","article-title":"Considerations on the serial pc-arduino uno r3 interaction, in java, using jdeveloper, for a 3r serial robot, based on the ardulink library","volume":"64","author":"Antal","year":"2018","journal-title":"Acta Tech. Napoc.-Ser. Appl. Math. Mech. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gutierrez-Giles, A., Padilla-Casta\u00f1eda, M.A., Alvarez-Icaza, L., and Gutierrez-Herrera, E. (2022). Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation. Sensors, 22.","DOI":"10.3390\/s22228670"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Pillai, B.M., Sivaraman, D., Ongwattanakul, S., and Suthakorn, J. (2022, January 17\u201320). Sensorless based gravity torque estimation and friction compensation for surgical robotic system. Proceedings of the 9th International Conference on e-Learning in Industrial Electronics, Brussel, Belgium.","DOI":"10.1109\/ICELIE55228.2022.9969429"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8805","DOI":"10.1109\/JSEN.2021.3052755","article-title":"Image-based force estimation in medical applications: A review","volume":"21","author":"Nazari","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_17","first-page":"11","article-title":"3R serial robot control based on arduino\/genuino uno, in java, using JDeveloper and Ardulink","volume":"61","author":"Antal","year":"2018","journal-title":"Acta Tech. Napoc.-Ser. Appl. Math. Mech. Eng."},{"key":"ref_18","unstructured":"Reyzabal, M.D.I., Malas, D., Wang, S., Ourselin, S., and Liu, H. (2024). SurgeMOD: Translating image-space tissue motions into vision-based surgical forces. arXiv."},{"key":"ref_19","unstructured":"Chua, Z., Jarc, A.M., and Okamura, A.M. (June, January 30). Toward force estimation in robot-assisted surgery using deep learning with vision and robot state. Proceedings of the IEEE International Conference on Robotics and Automation, Xi\u2019an, China."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s10846-024-02100-8","article-title":"A Stereovision-based Approach for Retrieving Variable Force Feedback in Robotic-Assisted Surgery Using Modified Inception ResNet V2 Networks","volume":"110","author":"Sabique","year":"2024","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jung, W.J., Kwak, K.S., and Lim, S.C. (2021). Vision-Based Suture Tensile Force Estimation in Robotic Surgery. Sensors, 21.","DOI":"10.3390\/s21010110"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Gao, Y., Lin, X., Peven, M., Unberath, M., and Austin, R. (2018). Learning to see forces: Surgical force prediction with RGB-point cloud temporal convolutional networks. Computer Assisted and Robotic Endoscopy Workshop, Springer International Publishing.","DOI":"10.1007\/978-3-030-01201-4_14"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1109\/TMECH.2024.3461968","article-title":"Unified Contact Model and Hybrid Motion\/force Control for Teleoperated Manipulation in Unknown Environments","volume":"30","author":"Huang","year":"2025","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7275","DOI":"10.1109\/TIE.2021.3095820","article-title":"Toward Sensorless Interaction Force Estimation for Industrial Robots Using High-Order Finite-Time Observers","volume":"69","author":"Han","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","unstructured":"(2024, October 08). Robotiq. Available online: https:\/\/robotiq.com\/products\/ft-300-force-torque-sensor#:~:text=The%20FT%20300-S%20Force%20Torque%20Sensor%20takes."},{"key":"ref_26","unstructured":"(2024, October 08). Endoscopic Camera. Available online: https:\/\/dothecamera.com\/product\/3-9mm-oh01a10-endoscope-camera-module-720p-60fps-for-medical\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cartucho, J., Ventura, R., and Veloso, M. (2018, January 1\u20135). Robust Object Recognition Through Symbiotic Deep Learning in Mobile Robots. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594067"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Baranwa, S.K., Jaiswal, K., Vaibhav, K., Kumar, A., and Srikantaswamy, R. (2020, January 15\u201317). Performance analysis of Brain Tumour Image Classification using CNN and SVM. Proceedings of the Second International Conference on Inventive Research in Computing Applications, Coimbatore, India.","DOI":"10.1109\/ICIRCA48905.2020.9183023"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Salehi, A.W., Khan, S., Gupta, G., Alabduallah, B.I., Almjally, A., Alsolai, H., Siddiqui, T., and Mellit, A. (2023). A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Future Scope. Sustainability, 15.","DOI":"10.3390\/su15075930"},{"key":"ref_30","unstructured":"Tan, M., and Le, Q.V. (2021, January 18\u201324). EfficientNetV2: Smaller Models and Faster Training. Proceedings of the 38th International Conference on Machine Learning, Virtual."},{"key":"ref_31","unstructured":"Qureshi, A., Shaikh, M., and Ahmad, A. (2018, January 18\u201319). Feature extraction using convolution neural networks (CNN) and deep learning. Proceedings of the 3rd International Conference on Computing, Mathematics and Engineering Technologies, Bangalore, India."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Panoiu, M., Panoiu, C., Mezinescu, S., Militaru, G., and Baciu, I. (2023). Machines Learning Techniques Applied to the Harmonic Analysis of Railway Power Supply. Mathematics, 11.","DOI":"10.3390\/math11061381"},{"key":"ref_36","first-page":"327","article-title":"Estimation of the Effort Required to Develop a Software through the K-Nearest Neighbors Method","volume":"66","author":"Iordan","year":"2023","journal-title":"Acta Tech. Napocesis-Ser. Appl. Math. Mech. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1007\/s42979-025-03954-x","article-title":"Prediction of Cardiovascular Disease using XGBoost with Optuna","volume":"6","author":"Jain","year":"2025","journal-title":"SN Comput. Sci."},{"key":"ref_38","unstructured":"(2024, October 09). NVIDIA. Available online: https:\/\/www.nvidia.com\/en-us\/design-visualization\/rtx-a6000\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Iordan, A.E. (2022, January 3\u20135). Usage of Stacked Long Short-Term Memory for Recognition of 3D Analytic Geometry Elements. Proceedings of the International Conference on Agents and Artificial Intelligence, Lisbon, Portugal.","DOI":"10.5220\/0010898900003116"},{"key":"ref_40","first-page":"65","article-title":"Classification of Diabetic Retinopathy Using ResNet50","volume":"9","author":"Sagala","year":"2025","journal-title":"J. Adv. Res. Electr. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s12672-025-03501-3","article-title":"Improved brain tumor classification through DenseNet121 based transfer learning","volume":"16","author":"Rasheed","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_42","first-page":"1","article-title":"Enhancing facial expression recognition using coordinate attention mechanism and MobileNetV3","volume":"84","author":"Bendelhoum","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"ref_43","first-page":"129","article-title":"Automated Classification of Coloboma Subtypes Using InceptionV3 Algorithm on Optical Coherence Tomography Images","volume":"10","author":"Kirankumar","year":"2025","journal-title":"J. Inf. Syst. Eng. Manag."},{"key":"ref_44","unstructured":"(2024, October 09). CPU. Available online: https:\/\/ark.intel.com\/content\/www\/us\/en\/ark\/products\/134599\/intel-core-i9-12900k-processor-30m-cache-up-to-5-20-ghz.html."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, H., Han, Y., Emerson, D., Rabin, Y., and Kara, L.B. (2025). A data-driven approach for real-time soft tissue deformation prediction using nonlinear presurgical simulations. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0319196"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pisla, D., Popa, C., Pusca, A., Ciocan, A., Gherman, B., Mois, E., Cailean, A., Vaida, C., Radu, C., and Chablat, D. (2023). On the Control and Validation of the PARA-SILSROB Surgical Parallel Robot. Appl. Sci., 14.","DOI":"10.3390\/app14177925"},{"key":"ref_47","unstructured":"Vaida, C., Gherman, B., Birlescu, I., Tucan, P., Pusca, A., Rus, G., Chablat, D., and Pisla, D. (July, January 30). Kinematic Analysis of a Parallel Robot for Minimally Invasive Surgery. Proceedings of the 19th International Symposium on Advances in Robot Kinematics, Ljubljana, Slovenia."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pisla, D., Tucan, P., Chablat, D., Al Hajjar, N., Ciocan, A., Pisla, A., Pu\u0219ca, A., Radu, C., Pop, G., and Gherman, B. (2024, January 5\u20137). Accuracy and Repeatability of a Parallel Robot for Personalised Minimally Invasive Surgery. Proceedings of the 33rd International Conference on Robotics in Alpe-Adria-Danube Region, Cluj-Napoca, Romania.","DOI":"10.1007\/978-3-031-59257-7_20"},{"key":"ref_49","unstructured":"(2024, October 09). 3D-Connexion. Available online: https:\/\/3dconnexion.com\/ro\/."},{"key":"ref_50","unstructured":"(2024, October 09). Omega 7. Available online: https:\/\/www.forcedimension.com\/products\/omega."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s00418-021-02059-9","article-title":"Abnormalities in esophageal smooth muscle induced by mutations in collagen XIX","volume":"157","author":"Sato","year":"2022","journal-title":"Histochem. Cell Biol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e2129228","DOI":"10.1001\/jamanetworkopen.2021.29228","article-title":"Comparison of Clinical Outcomes of Robot-Assisted, Video-Assisted, and Open Esophagectomy for Esophageal Cancer: A Systematic Review and Meta-analysis","volume":"4","author":"Mederos","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"B\u00fcdeyri, I., El-Sourani, N., Eichelmann, A.-K., Merten, J., Juratli, M.A., Pascher, A., and Hoelzen, J.P. (2024). Caseload per Year in Robotic-Assisted Minimally Invasive Esophagectomy: A Narrative Review. Cancers, 16.","DOI":"10.3390\/cancers16203538"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/993\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T15:15:22Z","timestamp":1763565322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/11\/993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,17]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["info16110993"],"URL":"https:\/\/doi.org\/10.3390\/info16110993","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,17]]}}}