The general CLINICCAI & MICCAI schedule can be found here
- Buses will be available at the end of CLINICCAI program to transport participants to the Gala Dinner
- Virtual attendance is possible, please register here and follow the conference!
- A panel discussion will close CLINICCAI, on the addressing the "key challenges of clinical translation of impactful AI solutions" (click here for more info)
- Prof. Inti Zlobec confirmed her presence on-site as keynote speaker during CLINICCAI conference, and we're looking forward to attending her lecture entitled "Tissue Medicine Goes Digital: How the Future of Pathology Will Influence Personalized Health"
- A Best Presentation Award sponsored by the Institute of Image-Guided Surgery (IHU-Strasbourg, France) will be assigned by an expert jury to the best work presented at CLINICCAI 2022
Welcome to CLINICCAI
Clinical Translation of Medical Image Computing and Computer Assisted Interventions
It is a great pleasure to invite you to the second edition of CLINICCAI, a MICCAI event dedicated to healthcare practitioners willing to discuss their research on 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.
The second edition of 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 2022, allowing participants to explore and get inspired by the other scientific sessions, workshops and social happenings of the biomedical conference.
In its second edition, CLINICCAI will be held from September 18th to 22nd 2022 in the Resort World Convention Centre Singapore. This will be the first MICCAI (and CLINICCAI!) conference hosted in Southeast Asia.
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 MICCAI2022 program.
We look forward to welcoming you at CLINICCAI!
CALL FOR ABSTRACTS
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 the second edition of CLINICCAI.
Abstracts should be structured according to the Submission Guidelines and should be submitted via Microsoft's CMT by May 13, 2022.
Submissions will be peer-reviewed by physicians in the CLINICCAI committees, and authors will be notified about the decision by June 24th, 2022.
Presenting authors must be healthcare practitioners (e.g., physicians, nurses, technicians) 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 and sponsored by the Institute of Image-Guided Surgery (IHU Strasbourg).
TOPICS OF INTEREST
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
- CLINICCAI conference
- September 20th, 2022
- Real-World Evaluation of a Semi-supervised Artificial-Intelligence Model Trained on 185,412 cells for Identification of White Blood Cells
Fan, Bingwen Eugene*; Chen, David Tao Yi; Binte Abdul Latiff , Siti Thuraiya; Lim, Eric Kian Guan; Ong, Yi Xiong; Wong, Moh Sim; Winkler, Stefan ; Kuperan , Ponnudurai
- Proposal and Multicentric Validation of a Laparoscopic Roux-en-Y Gastric Bypass Surgery Ontology
Lavanchy, Joël L.*; Gonzalez, Cristians; Kassem, Hasan; Nett, Philipp; Padoy, Nicolas
- Digital Quantification of Surgical Expertise & Training Through Full-Body Kinematics and Time Series Clustering
Nimer, Amr*; Rehman, Abdullah; Nandi, Dipankar; Faisal, Aldo
- Determining the effect of AI Assistance when scoring ki-67 on sarcomas
M, Logaswari*; Saraf, Sahil; Khor, Li Yan; Singh, Aahan; Selavarajan, Sathiyamoorthy; Lim, Kiat Hon; PV, Santhosh; Ravikumar, Vani; Somwanshi, Priyanka; Jialdasani, Rajasa; Taghipour, Kaveh; Sathe, Aneesh
- Deep-learning-based Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping
- Automated Anonymization of Robotic Surgical Video Data using Deep Learning
De Backer, Pieter*; Simoens, Jente; Mestdagh, Kenzo; Cisternino, Francesco; Ferraguti, Federica; D'Hondt, Mathieu; Fuchs, Hans; Debbaut, Charlotte; Decaestecker, Karel; Mottrie, Alex
- An automated approach for AI model validation on sub cohort analyses to assess for biases
Funahashi, Ray; Yorgancigil, Emre; Kocaman, Veysel; Hosgor, Enes; Ayers, Brian*
- Abdominal organ segmentation in minimally-invasive surgery - presenting the Dresden Surgical Anatomy Dataset
Fiona R. Kolbinger*, Sebastian Bodenstedt, Franziska M. Rinner, Matthias Carstens, Stefan Leger, Alexander C. Jenke, Thomas P. Nielen, Jürgen Weitz, Marius Distler, Stefanie Speidel
- AI-powered, biomarker-free activated T cells quantification at single-cell level: Proof-of-concept for cell therapy and diagnostic tool for T cells immunity
Ng, Chan Way; Lim, Chun Jye*; Peh, Khong Ming; Meng, Jia; Goh, Denise; Lim, Xinru; Lau, Mai Chan; Brack, Andrew; Yeong, Joe
- Risk Assessment After Myocardial Infarction Using Automated Left Ventricular Shape Analysis vs Myocardial Strains
Corral Acero, Jorge*; Schuster, Andreas; Eitel, Ingo; Zacur, Ernesto; Evertz, Ruben; Lange, Torben; Backhaus, Sören Jan; Stiermaier, Thomas; Thiele, Holger; Bueno-Orovio, Alfonso; Lamata, Pablo; Grau, Vicente
- Towards automatic detection in pancreatic EUS: an assessment of Deep Learning methods
Julieta Montanelli, Antoine Fleurentin, Adrien Meyer, Jean-Paul Mazellier, Lee Swanstrom, Benoit Gallix, Georgios Exarchakis, Leonardo Sosa Valencia, Nicolas Padoy
- Multi-center Evaluation of Machine Learning Models for Predicting Neo-adjuvant Chemotherapy Response in Breast Cancer
Tan Hong Qi*, Ong Hiok Hian, Arjunan Muthu Kumaran, Tira J. Tan, Ryan Shea Tan Ying Cong, Ghislaine Lee Su-Xin, Elaine Lim Hsuen, Raymond Ng Chee Hui, Richard Yeo Ming Chert, Faye Lynette Lim Wei Tching, Zhang Zewen, Christina Yang Shi Hui, Wong Ru Xin, Gideon Ooi Su Kai, Lester Leong Chee Hao, Tan Su Ming, Madhukumar Preetha, Sim Yirong, Veronique Tan Kiak Mien, Joe Yeong, Wong Fuh Yong, Cai Yiyu, Wen Long Nei
- Deep learning predicts somatic BRCA 1/2 genes mutational status from histopathology of epithelial ovarian cancer: a hypothesis generating study
Nero, Camilla; Boldrini, Luca; Lenkowicz, Jacopo; Giudice, Maria Teresa*; Piermattei, Alessia; Zannoni, Gianfranco; Pasciuto, Tina; Minucci, Angelo; Fagotti, Anna; Zannoni, Gianfranco; Valentini, Vincenzo; Scambia, Giovanni
- Feature translation between cone-beam and fan-beam computed tomography scans using cycle consistent generative adversarial networks
ho, zheng yi*; Tan, Hong Qi; Nei, Wen Long; Cai, Yiyu
- AI-powered Tumor infiltrating lymphocytes scoring: is there a potential for cross-cancer type validation?
Wee, Felicia*; Suresh, Nivedita; Lim, Jeffrey; Lim, Chun Jye; Lim, Xinru; Lau, Mai Chan; Yeong, Joe; Goh, Denise; Peh, Khong Ming; Ng, Chan Way; Zhu, Yong Qiang; Brack, Andrew
- Bringing Surgical Artificial Intelligence to End-Users: Development of a Platform for Live Intraoperative Inference
Madani, Amin*; Zhang, Haochi; Mashouri, Pouria; Hunter, Jaryd; Protserov, Sergey; Masino, Caterina; Laplante, Simon; Hashimoto, Daniel; Mascagni, Pietro; Alseidi, Adnan; Brudno, Michael
- Detecting Bias in Artificial Intelligence Models for Surgical Videos: Is the Model Predicting True Anatomy or Simply Following Surgical Instruments?
Madani, Amin*; Hunter, Jaryd; Mashouri, Pouria; Protserov, Sergey; Zhang, Haochi; Masino, Caterina; Namazi, Babak; Brudno, Michael
- Pulmonary Artery Detection in Thoracic Surgery Using Conditional Adversarial Networks
Mansur, Arian*; Manjanna, Sandeep; Verma, Rohan; Costantino, Christina; Yang, Chi-Fu Jeffrey; Chaudhari, Pratik; Schumacher, Lana
- Spatial analysis using morphology-transcriptome-defined cell phenotypes with machine learning
Lau, Mai Chan*; Yan, Wei; Luong , Hien Nga; Azam , Abu Bakr ; Lim , Jeffrey Chun Tatt; Yeong, Joe; Cai, Yiyu
- Video assessment as a tool to analyze surgical technique: Catheter insertion during routine intra-operative cholangiogram in an academic setting
Monika E. Hagen*, Lela Dimonte, Jonathan Douissard, Alexis Litchinko, Sofia El Hajji, Mickael Chavallay, Florence Latini, Verun Goel, Rami Abukhalil, Pablo Garcia, Christian Toso
- Prof. Jason Chan, MD
- The Chinese University of Hong Kong, China
- Prof. Heike I. Grabsch, MD, PhD
- University of Leeds, UK, and Maastricht University, Netherlands
- Prof. Nicolas Padoy, PhD
- University of Strasbourg & Institute of Image-Guided Surgery, IHU Strasbourg, France
- Daniel Hashimoto, MD
- University of Pennsylvania, Philadelphia, PA, USA
- Xiao Li, MD
- Peking University People's Hospital, China
- Pietro Mascagni, MD
- Fondazione Policlinico Universitario A. Gemelli, Italy & Institute of Image-Guided Surgery, IHU Strasbourg, France
- Tiffany So, MD
- The Chinese University of Hong Kong, China
- Juan Manuel Verde, MD
- Institute of Image-Guided Surgery, IHU Strasbourg, France
- Yi Yang, MD
- Beijing Tiantan Hospital, China
- Joe Yeong, MD
- Singapore General Hospital, IMCB, A*STAR, Singapore
- Alain Garcia
- IHU Strasbourg
- Barbara Seeliger
- IHU Strasbourg
- Duygu Sarikaya
- Ivo Boskoski
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Jurgen Futerer
- Lit-Hsin Loo
- Bioinformatics Institute, IMCB, A*STAR, Singapore
- Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Mai Chan Lau
- IMCB, A*STAR, Singapore
- Manish Chand
- University College London
- Marco Zenati
- Harvard Medical School
- Regina Beets-Tan
- The Netherlands Cancer Institute
- Roi Anteby
- The Massachusetts General Hospital
- Swaroop Vedula
- The Johns Hopkins University
- Varut Vardhanabhuti
- The University of Hong Kong
- 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.
About 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
PLEASE CLICK HERE TO DOWNLOAD CLINICCAI TEMPLATE
- At least two communities are involved in the research and development of biomedical AI, the "methodologists" gathering AI-experts and computer scientists, and the "domain experts", including clinicians and other healthcare professionals, and CLINICCAI was conceived to catalyze their interactions and cross-pollination
- Nowadays, and regardless of the tremendous efforts and resources allocated to AI research, few solutions transitioned to clinical scenarios and improved outcomes
- This panel session aims at discussing further underlying issues, challenges, and open questions, from different viewpoints
- Clinicians: Inti Zlobec, Heike Grabsch, and Jason Chan
- Computer Scientists: Nassir Navab, Danail Stoyanov, and Nicolas Padoy
- Moderators: Joe Yeong and Daniel Hashimoto
Topics to be discussed:
- data collection and annotation
- application and validation
- generalizability and overfitting
- AI explainability