{"id":2358,"date":"2024-03-09T15:39:31","date_gmt":"2024-03-09T15:39:31","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/elvis-chen\/"},"modified":"2024-03-09T15:39:31","modified_gmt":"2024-03-09T15:39:31","slug":"elvis-chen","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/elvis-chen\/","title":{"rendered":"Elvis Chen"},"content":{"rendered":"
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Elvis Chen<\/h1>\n<\/div>\n
Postdoctoral Fellow<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from 2008<\/em> to 2009<\/em><\/div>\n
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Reinke, Annika; Li, Z.; Tizabi, Minu D.; Andr\u00e9, Pascaline; Knopp, Marcel; Rother, Mika M.; Machado, In\u00eas; Altieri, Maria S.; Alapatt, Deepak; Bano, Sophia; Bodenstedt, Sebastian; Burgert, Oliver; Chen, Elvis C. S.; Collins, Justin; Colliot, Olivier; Christodoulou, Evangelia; Czempiel, Tobias; Das, Adrito; Docea, Reuben; Donoho, Daniel A.; Dou, Qi; Eckhoff, Jennifer A.; Engelhardt, Sandy; Fichtinger, G\u00e1bor; Fuernstahl, Philipp; Kilroy, Pablo Garc\u00eda; Giannarou, Stamatia; Gilbert, Stephen; Gockel, Ines; Godau, Patrick; G\u00f6deke, Jan; Grantcharov, Teodor; Haidegger, Tam\u00e1s; Hann, Alexander; Hashizume, Makoto; Heitz, Charles; Hisey, Rebecca; Hoffmann, Hanna; Huaulm\u00e9, Arnaud; J\u00e4ger, Paul F.; Jannin, Pierre; Jarc, Anthony; Jena, Rohit; Jin, Yueming; Joskowicz, Leo; Joyeux, Luc; Kirchner, Max; Krieger, Axel; Kronreif, Gernot; Lam, Kyle; Laufer, Shlomi; Lavanchy, Jo\u00ebl L.; Lee, G; Lim, Robert H.; Liu, Peng; Luttner, Lucas; Marcus, Hani J.; Mascagni, Pietro; Mayer, Leon; Meireles, Ozanan R.; Mueller, Beat P.; M\u00fcndermann, Lars; Nakawala, Hirenkumar; Navab, Nassir; Ndong, Abdourahmane; Neumann, Juliane; Nickel, Felix; Nolden, Marco; Nwoye, Chinedu; Oh, Namkee; Padoy, Nicolas; R\u00e4dsch, Tim; Pfeiffer, Micha; R\u00e4dsch, Tim; Ren, Hongliang; Rieke, Nicola; Rivoir, Dominik; Sarikaya, Duygu; Schmidgall, Samuel; Seibold, Matthias; Seidlitz, Silvia; Seitel, Alexander; Sharan, Lalith; Siewerdsen, Jeffrey H.; Srivastav, Vinkle; Sznitman, Raphael; Taylor, Russell; Tran, Thuy N.; Unberath, Matthias; Sommen, Fons; Wagner, Martin; Yamlahi, Amine; Zhou, Shaohua K.; Zia, Aneeq; Madani, Amin; Stoyanov, Danail; Speidel, Stefanie; Hashimoto, Daniel A.; Kolbinger, Fiona R.; Maier\u2010Hein, Lena<\/p>

Current validation practice undermines surgical AI development Journal Article<\/span> <\/p>

In: <\/span>PubMed, <\/span>2025<\/span>.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{reinke2025,
\r\ntitle = {Current validation practice undermines surgical AI development},
\r\nauthor = {Annika Reinke and Z. Li and Minu D. Tizabi and Pascaline Andr\u00e9 and Marcel Knopp and Mika M. Rother and In\u00eas Machado and Maria S. Altieri and Deepak Alapatt and Sophia Bano and Sebastian Bodenstedt and Oliver Burgert and Elvis C. S. Chen and Justin Collins and Olivier Colliot and Evangelia Christodoulou and Tobias Czempiel and Adrito Das and Reuben Docea and Daniel A. Donoho and Qi Dou and Jennifer A. Eckhoff and Sandy Engelhardt and G\u00e1bor Fichtinger and Philipp Fuernstahl and Pablo Garc\u00eda Kilroy and Stamatia Giannarou and Stephen Gilbert and Ines Gockel and Patrick Godau and Jan G\u00f6deke and Teodor Grantcharov and Tam\u00e1s Haidegger and Alexander Hann and Makoto Hashizume and Charles Heitz and Rebecca Hisey and Hanna Hoffmann and Arnaud Huaulm\u00e9 and Paul F. J\u00e4ger and Pierre Jannin and Anthony Jarc and Rohit Jena and Yueming Jin and Leo Joskowicz and Luc Joyeux and Max Kirchner and Axel Krieger and Gernot Kronreif and Kyle Lam and Shlomi Laufer and Jo\u00ebl L. Lavanchy and G Lee and Robert H. Lim and Peng Liu and Lucas Luttner and Hani J. Marcus and Pietro Mascagni and Leon Mayer and Ozanan R. Meireles and Beat P. Mueller and Lars M\u00fcndermann and Hirenkumar Nakawala and Nassir Navab and Abdourahmane Ndong and Juliane Neumann and Felix Nickel and Marco Nolden and Chinedu Nwoye and Namkee Oh and Nicolas Padoy and Tim R\u00e4dsch and Micha Pfeiffer and Tim R\u00e4dsch and Hongliang Ren and Nicola Rieke and Dominik Rivoir and Duygu Sarikaya and Samuel Schmidgall and Matthias Seibold and Silvia Seidlitz and Alexander Seitel and Lalith Sharan and Jeffrey H. Siewerdsen and Vinkle Srivastav and Raphael Sznitman and Russell Taylor and Thuy N. Tran and Matthias Unberath and Fons Sommen and Martin Wagner and Amine Yamlahi and Shaohua K. Zhou and Aneeq Zia and Amin Madani and Danail Stoyanov and Stefanie Speidel and Daniel A. Hashimoto and Fiona R. Kolbinger and Lena Maier\u2010Hein},
\r\nyear  = {2025},
\r\ndate = {2025-01-01},
\r\njournal = {PubMed},
\r\nabstract = {Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation emerging as an important contributing factor. In fact, existing validation practices often neglect the temporal and hierarchical structure of intraoperative videos, producing misleading, unstable, or clinically irrelevant results. In a pioneering, consensus-driven effort, we introduce a comprehensive catalog of validation pitfalls in AI-based surgical video analysis that was derived from a multi-stage Delphi process with 92 international experts. The collected pitfalls span three categories: (1) data (e.g., incomplete annotation, spurious correlations), (2) metric selection and configuration (e.g., neglect of temporal stability, mismatch with clinical needs), and (3) aggregation and reporting (e.g., clinically uninformative aggregation, failure to account for frame dependencies in hierarchical data structures). A systematic review of surgical AI papers reveals that these pitfalls are widespread in current practice, with the majority of studies failing to account for temporal dynamics or hierarchical data structure, or relying on clinically uninformative metrics. Experiments on real surgical video datasets provide empirical evidence that ignoring temporal and hierarchical data structures can substantially understate uncertainty, obscure critical failure modes, and even alter algorithm rankings. To address these shortcomings, we provide a catalogue of best practices compiled in a multi-stage Delphi process. Together, this work provides an evidence-based framework to inform more rigorous validation of surgical video analysis algorithms and to guide future efforts in benchmarking, reporting, regulatory review, and clinical translation.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Surgical data science (SDS) is rapidly advancing, yet clinical adoption of artificial intelligence (AI) in surgery remains limited, with inadequate validation emerging as an important contributing factor. In fact, existing validation practices often neglect the temporal and hierarchical structure of intraoperative videos, producing misleading, unstable, or clinically irrelevant results. In a pioneering, consensus-driven effort, we introduce a comprehensive catalog of validation pitfalls in AI-based surgical video analysis that was derived from a multi-stage Delphi process with 92 international experts. The collected pitfalls span three categories: (1) data (e.g., incomplete annotation, spurious correlations), (2) metric selection and configuration (e.g., neglect of temporal stability, mismatch with clinical needs), and (3) aggregation and reporting (e.g., clinically uninformative aggregation, failure to account for frame dependencies in hierarchical data structures). A systematic review of surgical AI papers reveals that these pitfalls are widespread in current practice, with the majority of studies failing to account for temporal dynamics or hierarchical data structure, or relying on clinically uninformative metrics. Experiments on real surgical video datasets provide empirical evidence that ignoring temporal and hierarchical data structures can substantially understate uncertainty, obscure critical failure modes, and even alter algorithm rankings. To address these shortcomings, we provide a catalogue of best practices compiled in a multi-stage Delphi process. Together, this work provides an evidence-based framework to inform more rigorous validation of surgical video analysis algorithms and to guide future efforts in benchmarking, reporting, regulatory review, and clinical translation.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
 Holden, M.;  Ungi, Tamas;  Sargent, D.;  McGraw, Robert C;  Chen, Elvis C. S.;  Ganapathy, Sugantha;  Peters, T. M.;  Fichtinger, Gabor<\/p>
Feasibility of Real-Time Workflow Segmentation for Tracked Needle Interventions<\/a> Journal Article<\/span> <\/p>
In: <\/span>IEEE Transactions on Biomedical Engineering, <\/span>vol. 61, <\/span>no. 6, <\/span>pp. 1720-1728, <\/span>2014<\/span>, ISSN: 0018-9294<\/span>.<\/p>
Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@article{Holden2014a,
\r\ntitle = {Feasibility of Real-Time Workflow Segmentation for Tracked Needle Interventions},
\r\nauthor = {M. Holden and Tamas Ungi and D. Sargent and Robert C McGraw and Elvis C. S. Chen and Sugantha Ganapathy and T. M. Peters and Gabor Fichtinger},
\r\nurl = {https:\/\/labs.cs.queensu.ca\/perklab\/wp-content\/uploads\/sites\/3\/2024\/02\/Holden2014a-Manuscript.pdf},
\r\ndoi = {10.1109\/TBME.2014.2301635},
\r\nissn = {0018-9294},
\r\nyear  = {2014},
\r\ndate = {2014-06-01},
\r\nurldate = {2014-06-01},
\r\njournal = {IEEE Transactions on Biomedical Engineering},
\r\nvolume = {61},
\r\nnumber = {6},
\r\npages = {1720-1728},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>