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The general CLINICCAI & MICCAI schedule can be found here.

The first CLINICCAI Best Presentation Award consisting in a check and a 2-day fully funded research visit at the sponsoring Institute of Image-Guided Surgery, IHU Strasbourg, goes to:

  • Fiona Kolbinger, for the work:
    • Artificial Intelligence for context-aware surgical guidance during robot-assisted rectal resection - an exploratory feasibility study
      F. Kolbinger, S. Leger, M. Carstens, F. M. Rinner, S. Krell, A. Chernykh, S. Bodenstedt, J. Fritzmann, M. Distler, J. Weitz, S. Speidel
  • Cesare Hassan, for the work:
    • Optical diagnosis assisted by real-time Artificial Intelligence for < 5 mm rectosigmoid polyps (Artificial intelligence BLI Characterization; ABC study)
      E. Rondonotti, C. Hassan, G. Tamanini, G. Antonelli, G. Andrisani, G. Leonetti, S. Paggi, A. Amato, G. Scardino, D. Di Paolo, G. Mandelli, N. Lenoci, N. Terreni, A. Andrealli, R. Maselli, M. Spadaccini, P. A. Galtieri, L. Correale, A. Repici, F.M. Di Matteo, L. Ambrosiani, E. Filippi, P. Sharma, F. Radaelli


  • An award sponsored by the IHU Strasbourg will be given to the best work, including the abstract and its presentation during CLINICCAI conference (further information will be provided soon)
  • The review process ended and here you can find the titles and authors of the accepted abstracts. The final program will be published soon
  • Prof. Richard M. Satava confirmed his presence as a joint keynote speaker during MICCAI & CLINICCAI conference

Welcome to CLINICCAI - Clinical Translation of Medical Image Computing and Computer Assisted Interventions

Dear Colleagues,

It is a great pleasure to invite you to the very first edition of CLINICCAI, a MICCAI event dedicated to healthcare practitioners willing to discuss their research on the more translational and clinical aspects of medical image computing, computer-assisted interventions, and medical robotics.

The Medical Image Computing and Computer Assisted Intervention Society (the MICCAI Society) is a leading community of biomedical scientists, engineers, and clinicians working on advances in the methodology and applications of these fields since 1998. Recent methodological improvements and new clinical applications enabled by breakthroughs in medical imaging, deep learning and other AI techniques motivated MICCAI to create a clinical day to reinforce its clinical ties and explore further how to generate value for patients and healthcare systems.

CLINICCAI will be a fantastic opportunity for healthcare practitioners to share their translational research experiences, discuss needs with biomedical researchers with diverse backgrounds, network and become active members of the growing MICCAI community. The event will take place in parallel to MICCAI 2021, allowing participants to explore and get inspired by the other scientific sessions, workshops and social happenings of the biomedical conference.

In this first edition, CLINICCAI will be a hybrid event - allowing both onsite and online participation - as the rest of MICCAI2021. It will be held in Strasbourg, France, on September 29, 2021. Works to be presented will be selected based on an abstract submission evaluated by an international committee of physicians. Renowned practitioners in the field will be invited for keynote lectures in a joint session with the main MICCAI2021 program.

We look forward to welcoming you at CLINICCAI!

Nicolas Padoy, PhD - Lee Swanström, MD - General Chairs.


Healthcare practitioners involved in multidisciplinary research teams or start-ups are kindly invited to submit their translational research on medical image computing, computer-assisted intervention, and medical robotics for presentation at CLINICCAI.

Abstracts should be structured according to the Submission Guidelines and should be submitted via Microsoft's CMT by May 24, 2021 June 7, 2021.

Submissions will be reviewed by physicians in the CLINICCAI Committees and authors will be notified about the decision by June 28, 2021.

Presenting authors must be healthcare practitioners committed to register to the conference (discounted registration). All presentations will compete for the CLINICCAI Best Presentation Award, which will be announced at the end of the event.

Authors will have the opportunity of publishing their abstracts in proceedings and/or proceed with a fast-tracked submission of a full paper in Surgical Innovation.


Topics of interest to CLINICCAI include, but are not limited to:

  • Digital patient
  • Digital pathology
  • Computer aided diagnosis
  • Predictive modelling of risks, diseases and patients' outcomes
  • Advanced preoperative planning and surgical guidance
  • Image-guided interventions
  • Immersive technologies in surgery (mixed, augmented and virtual reality)
  • Technologies to enhance patient safety and quality improvement
  • Video-based assessment of surgical procedures
  • Automated skill assessment
  • Ergonomics and human factors in surgery
  • Team dynamics assessment
  • Surgical data science
  • Digital surgery
  • Healthcare robotics
  • Virtual reality simulators
  • Surgical coaching
  • Serious gaming for training
  • Hospital and OR management systems
  • Device and strategies for OR translation

Abstract submission deadline:
23:59 PST, May 24th, 2021
Extended abstract submission deadline:
23:59 PST, June 7th, 2021
Notification of rejection or acceptance:
23:59 PST, June 28th, 2021
CLINICCAI Conference:
September 29, 2021

  • Geographic Atrophy Progression Prediction With Distinct Convolutional Neural Network Architectures Results In Differential Gradient Activation Maps.
    Ferrara, Daniela*; Cluceru, Julia; Anegondi, Neha; Steffen, Verena; Gao, Simon
  • Digital Biomarkers Of Ed Clinician's Professional Fulfillment, Burnout, Sleep Quality, Moral Injury And Ptsd During The Covid-19 Pandemic.
    Dias, Roger Daglius*; Eyre, Andrew; Miccile, Christian; Toutin Dias, Gabriela; Pozner, Charles
  • Using Computer Vision To Capture Surgical Team Motion During Pre-Incision Time-Out.
    Dias, Roger Daglius*; Kennedy-Metz, Lauren Rose; Gombolay, Matthew; Yule, Steven; Zenati, Marco A
  • Optical Diagnosis Assisted By Real-Time Artificial Intelligence For < 5 Mm Rectosigmoid Polyps Artificial Intelligence Bli Characterization; Abc Study.
    Rondonotti, Emanuele; Antonelli, Giulio*; Hassan, Cesare; Radaelli, Franco
  • Cost-Effectiveness Of Artificial Intelligence For Screening Colonoscopy.
    Antonelli, Giulio*; Hassan, Cesare
  • Artificial Intelligence In The Operating Room: Is Automated Surgical Reporting A Viable And Realistic Application? - A Proof Of Concept.
    Vogel, Thomas; Berlet, Maximilian W.*; Ostler, Daniel; Czempiel, Tobias; Kähler, Moritz; Brunner, Stephan; Feussner, Hubertus; Wilhelm, Dirk; Kranzfelder, Michael
  • Irradiation Dose Characterization And Validation Of A Fast Monte Carlo Simulation In X-Ray Guided Interventions.
    Nasr, Bahaa*; Villa Area, Mateo; Visvikis, Dimitris; Bert, Julien
  • Cross Center Validation Of Deep Learning For Automatic Segmentation Of Pelvic Bone Using Mri Images Of 1.5t And Mri Images Of 3t - External Validation Study.
    Lerch, Till*; Zeng, Guodong; Boschung, Adam ; Gerber, Nicolas ; Ruckli, Adrian; Grob, Valentin; Steppacher, Simon; Siebenrock, Klaus; Tannast, Moritz; Schmaranzer, Florian
  • Minimising Subjectivity In Surgery - An Automl Approach For Intraoperative Fluorescence Angiography.
    Soares, António S*; Bano, Sophia Dr; Clancy, Neil T; Lovat, Laurence; Stoyanov, Danail; Chand, Manish
  • Artificial Intelligence For Context-Aware Surgical Guidance During Robot-Assisted Rectal Resection - An Exploratory Feasibility Study.
    Kolbinger, Fiona*
  • Clinical Translation: Development And Performance Testing Of A Generalizable And Reproducible High-Grade Glioma Mri Segmentation And Volumetric Analysis Deep-Learning Network For Longitudinal Assessment To Clinically Inform The Modified Response Assessment In Neuro-Oncology Criteria.
    Abayazeed, Aly*
  • Can Machine Learning Be Used To Automatically Classify Glenoid Arthritis Patterns?
    Gauci, Marc-Olivier Mog*; Urvoy, Manuel; Guerre, Alexandre; Boileau, Pascal; Sanchez-Sotelo, Joaquin; Athwal, George; Walch, Gilles
  • Validation Of Automatic Assessment Of Shoulder Range Of Motion In Clinics: Combining Rgb-D Caméra Rgbd And Artificial Intelligence.
    Gauci, Marc-Olivier Mog*; Olmos, Manuel I; Chammas, Pierre-Emmanuel; Cointat, Caroline; Urvoy, Manuel; Murienne, Albert; Bronsard, Nicolas; Gonzalez, Jean-François
  • Cal-Obs, A Prospective Study Investigating The Use Of Dimensionless Square Jerk For The Assessment Of Expertise In Obstetric Ultrasound
    Dromey, Brian*; Vasconcelos, Francisco; Neary-Zajiczek, Lydia; David, Anna L.; Stoyanov, Danail; Peebles, Donald
  • Applications Of Mixed Reality In Minimally Invasive Surgery: Surgical Planning And Head-Mounted Display Laparoscopic Monitor.
    Plaza De Miguel, Carlos*; Sánchez Margallo, Juan Alberto ; Fernández Anzules, Roberto A.; Sánchez-Margallo, Francisco M.
  • Impact Of Deep-Learning-Based Abnormality Detection Tool On Breast Cancer Screening Workflow.
    Hurstel, Florie; Tardy, Mickael*; Mateus, Diana; Moliere, Sebastien
  • Implementation Of Personalized Medicine In Cutaneous Melanoma Patients Aided By Artificial Intelligence.
    Podlipnik, Sebastián*; Hernandez, Carlos; Kiroglu, Anil; Garcia, Sergio; Ficapal, Joan; Burgos, Julio; Calbet, Neus; Puig, Susana; Malvehy, Josep; Vilaplana, Veronica; Combalia, Marc
  • Surgomics - Personalized Prediction Of Outcomes In Surgical Oncology Using Machine Learning From Multimodal Data.
    Wagner, Martin*, Brandenburg JM, Bodenstedt S, Jenke AC, Stern A, Schulze A, Kolbinger FR, Bhasker N, Heinze O, Schneider G, Probst P, Mündermann L, Fallert J, Distler M, Weitz J, Müller-Stich BP, Speidel S
  • Development Of A Method For Personalised, Algorithmic Colonic Transection Recommendation.
    Dalli, Jeffrey*; Epperlein, Jonathan P; Hardy, Niall P; Khan, Mohammad Faraz; Zhuk, Sergiy; Mac Aonghusa, Pól G; Cahill, Ronan
  • Automated Detection Of Critical Anatomical Structures For The Assessment Of Completion Of Hernia Sac Dissection Step In Laparoscopic Hiatal Hernia Repair.
    Holcomb, Carla; Namazi, Babak*; Ward, Marc; Leeds, Steven; Scott, Daniel J.; Sankaranarayanan, Ganesh
  • Clinical Evaluation And Multi-Class Delineation Of A Multi-Centric Covid-19 Ai-Based Segmentation Study.
    Bajercius, Herkus*; Garcia Henao, John Anderson; Depotter, Arno; Barroso, Maria Cecilia; Duncan, James S; Sverzellati, Nicola; Cortopassi, Isabel; Hautz, Wolf; Dela Cruz, Charles; Silva, Mario; Caminiti, Caterina; He, George; Staib, Lawrence H; You, Chenyu; Yang, Junlin; Gange, Christopher; Schroeder, Erich; Pahud De Mortanges, Aurélie; Poellinger, Alexander ; Reyes, Mauricio
  • Automated Quantification Of Internal Kidney Tumor Edge Differential (Ited) Is A Significant Predictor Of Tumor Characteristics And Surgical Outcomes.
    Vasdev, Ranveer*; Mcsweeney, Sean; Heller, Nicholas E; Stai, Bethany; Tejapaul, Resha; Rickman, Jack; Regmi, Subodh; Papanikolopoulos, Nikolaos; Weight, Christopher
  • Using Semi-Supervised Learning To Annotate Intraoperative Anatomy In Surgical Videos: Decreasing Annotator Burden For Surgical Artificial Intelligence.
    Madani, Amin*; Namazi, Babak; Sankaranarayanan, Ganesh; Alseidi, Adnan; Hashimoto, Daniel A

  • Nicolas Padoy, PhD
    University of Strasbourg, France & IHU Strasbourg, France
  • Lee Swanström, MD
    IHU Strasbourg, France & Oregon Health and Science University, USA
  • Benoît Gallix, MD
    University Hospital of Strasbourg, France & IHU Strasbourg, France
  • WooJin Hyung, MD
    Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
  • Greg Osgood, MD
    Johns Hopkins Medicine, Baltimore, USA
  • Dirk Wilhelm, MD
    Klinikum Rechts der Isar, Munich, Germany
  • Juan Verde, MD
    IHU Strasbourg, France
  • Alain Garcia, MD
    IHU Strasbourg, France
  • Pietro Mascagni, MD
    Fondazione Policlinico Universitario A. Gemelli, Rome, Italy & IHU Strasbourg, France
  • Amir Szold
    Assia Medical Group
  • Anna L. David
    University College London
  • Baptiste Vasey
    University of Oxford
  • Barbara Seeliger
    IHU Strasbourg
  • Beat Müller-Stich
    University of Heidelberg
  • Cesare Hassan
  • Cristians Gonzalez
    IHU Strasbourg
  • Daniel A. Hashimoto
    Massachusetts General Hospital
  • Hani Marcus
    National Hospital for Neurology and Neurosurgery
  • Ivo Boskoski
    Fondazione Policlinico Gemelli IRCCS
  • Jurgen Futterer
  • Lorenzo Cobianchi
    University of Pavia
  • Luca Ansaloni
    University of Pavia
  • Luca Boldrini
    Fondazione Policlinico Universitario Agostino Gemelli IRCCS
  • Manish Chand
    University College London
  • Marco A. Zenati
    Harvard Medical School
  • Michael Kostrzewa
    Kantonsspital Baden
  • Nicolas Bourdel
    Université Clermont Auvergne
  • Nobuhiko Hata
    Harvard University
  • Philip Wai Y. Chiu
    The Chinese University of Hong Kong
  • Regina Beets
    Tan - The Netherlands Cancer Institute
  • Roger Daglius Dias
    Stratus Center for Medical Simulation, Brigham and Women's Hospital, Harvard Medical School
  • Roi Anteby
    The Massachusetts General Hospital
  • Swaroop Vedula
    The Johns Hopkins University
  • Shelly Soffer
    Samson Assuta Ashdod University Hospital
  • Silvana Perretta
    IHU Strasbourg
  • Teodor Grantcharov
    Surgical Safety Technologies
  • Xin Wang
    West China Hospital, Sichuan University


CLINICCAI will consider original works focusing on the preclinical or clinical translation of medical image computing, computer-assisted interventions, and healthcare robotics. Submissions should be original, written in standard English, and limited to a maximum of 600 words. The first and presenting author should be a healthcare practitioner.


The format:

  • Full title: The title should be concise, specific, and informative. Please limit the length of the title to 150 characters. Whenever possible include study type (e.g., first-in-animal, first-in-human, clinical trial, etc.).
  • Authors: The first author should be the healthcare practitioner presenting at CLINICCAI.
  • Affiliations: Please limit the affiliations to 2 per author.
  • Presenting author: Please provide the full name, affiliation, and contacts (email, phone number) of the presenting author.
  • Keywords: Please provide 3-5 keywords.
  • Key information: Please to 100 words.
    • Research question: aim based on the study hypothesis or goal/purpose.
    • Findings: results focused on primary outcome(s) and finding(s).
    • Meaning: key conclusion and implication.
  • Manuscript: Please limit to 600 words.
    • Introduction: Please summarize the context, the addressed clinical need, the hypothesis, and aim of your work.
    • Material and methods: Please include a description of the device (development, performance, etc.) being tested, the experimental setting, study outcomes, and analysis.
    • Results: Please avoid referencing results not yet available at the time of submission. Tables, pictures, and illustrations should be in a separate page at the end of the manuscript.
    • Discussion and Conclusion: Please emphasize new and important findings and aspects of the study, and the conclusions to be drawn, focusing on the potential to impact clinical care. Include the limitations and propose improvements whenever possible.
  • References: A total of 10 references following the AMA style (10thed).
  • Disclosures: Please explicit any potential conflict of interest handled during the work.