dataesr/scientific-paragraphs-categorization huggingface dataset:
An additional benefit of this approach is that it can be combined with real-time data acquisition regarding how surgeons interact with the risk stratification data, which would greatly facilitate the development of both predictive and prescriptive decision support tools. Importantly, presenting statistical data and evidence-based risk stratification information to the surgeon would also have a different regulatory path than a prescriptive SDS product that offers surgical decisions based on an AI database grounded in surgical decision making. The data-focused product leaves the surgeon fully responsible, while the decision based product makes it questionable who is fully responsible if the surgeon followed an AI-based decision and there was a poor outcome. Another benefit of focusing on descriptive technologies is there is a much smaller technology adoption hurdle for the surgeon when faced with trusting descriptive statistics compared to an AI-based prescriptive decision support tool.
An ML-based descriptive low-hanging fruit could be datadriven surgical reporting and documentation. Surgical procedures are currently documented as one to two pages of text. While a six to eight hour video will not serve as a report in itself, SDS may help extract relevant information from this video by automatically documenting important steps in the procedure. Here, computer vision algorithms for recognition of surgical phases and instruments may be used to extract metainformation from videos  (Mascagni et al. , 2021b ). An ML-based predictive low-hanging fruit could lie in the optimization of OR logistics. Prediction of procedure time either preoperatively or utilizing intraoperative sensor data may not improve patient outcome, but could provide value to hospital managers if it helps cut down costs in the OR by optimizing patient volume  (Aksamentov et al.
Since such an SDS application warns clinical staff but does not perform an action on the patient itself, the risk for the patient is inherently low. Here, a combination of surgical knowledge (which objects are at what time introduced into the patient's body?) with SDS algorithms (which objects can robustly be detected?) and an unobtrusive user interface with a low false alarm rate may result in a low-hanging fruit. Along these lines, automation of the surgical checklist  (Conley et al. , 2011)  would be a technically feasible SDS application with high clinical value. The impending success of next-generation surgical robotics in the OR may bring further opportunities to the clinical translation of SDS. The da Vinci ® surgical system (Intuitive Surgical Inc. , Sunnyvale, CA, USA) and its upcoming competitors lay the foundation for systematic data capture as well as surgical guidance by information augmentation in the OR.
Mission IV: Clinical translation Promote a cultural shift toward SDS-driven clinical practice. What is needed is thus a cultural shift towards data acqui-sition, annotation and analysis within a well-defined data governance framework as a primary clinical task  (August et al. , 2021). The allocation of economic, infrastructural and personnel resources within hospitals for this appears as a nonnegotiable requirement for the purpose. The need for creating value from large amounts of representative data, both for de novo development/validation and external validation studies, further necessitates multi-institutional collaborations. Researchers in other domains have achieved such collaborations, for example in genomics and bioinformatics; SDS would benefit from adopting relevant aspects of these domains' research cultures.
In addition, enabling explicit academic recognition for developing rigorously annotated data sets can facilitate data resources for research in SDS, as discussed in Sec. 4. Paving the way for short-term clinical success stories as well as long-term clinical translation further requires SDS applications to be integrated into clinical workflows. In fact, the sparsity of studies on SDS solutions for intraoperative care illustrate the challenge of conducting multi-disciplinary research while prioritizing the patient. Therefore, research on SDS products should consider the impact on workflow early in product development and closely engage relevant stakeholders (see Tab. 1). Impactful success stories could then be generated by focusing on low-hanging fruit presented in the previous section. These, in turn, would contribute to building public trust in SDS and boost public enthusiasm to spark patient demand.
Goals How to improve knowledge transfer among different stakeholders? (goal 4. 3) The creation of interdisciplinary networks involving the different stakeholders and the regular organization of SDS events in conjunction with both technical and medical conferences is key to improving knowledge transfer between the groups. Such events should, in part, be dedicated to specific questions, such as annotation guidelines, data structures or good practices with respect to external validation. As a means for actively disseminating, discussing, and promoting new insights in the field of SDS, a well-curated community web platform should be established as the central information hub. One could even go further and offer e. g. a prize for clinical trials demonstrating SDS success. A good means for public outreach could be the hosting of public days focused on a particular topic at major conferences in the field, as a way of creating awareness for that topic, or campaigns e.
g. in the vein of "Stop the Bleed" (ACS Committee on Trauma). How to train key SDS personnel? (goal 4. 4) In order to facilitate clinical translation of SDS in the long term, it will further be crucial to promote the transdisciplinary training of future surgical data scientists and thereby establish SDS as a career path. Computer scientists will have to enter operating rooms on a regular basis to understand real clinical problems and to get an impression of the obstacles in clinical translation. Similarly, surgeons will have to understand the basic principles, capabilities and limits of data science techniques to identify solvable clinical problems and proper applications for SDS. A viable path to improve knowledge transfer would be to establish SDS as a commonly respected career path in hospitals.
In this context, both technical and clinical disciplines should be complemented by knowledge and expertise in clinical research methodology, i. e. , epidemiology and biostatistics. Moreover, human factors engineering and human computer interaction researchers should be integrated into the community. Setting up such an SDS career path should also involve the definition of specifics and skills an 'AI-ready' clinician should meet. A curriculum should put a specific focus on medical statistics covering confounding variables, risk correction and data biases, as well as on regulatory issues (e. g. SaMD). On top of the research-oriented positions, we should further seek to establish SDS-related jobs for data acquisition, management and annotation, specifically in university hospitals. How to ensure high-quality external validation of SDS applications? (goal 4.
ML techniques offer the possibility to capture data patterns that may serve as potential surrogate measures of long-term outcomes. For example, many established metrics, such as 5-year-survival, are not immediately available after a surgical intervention for cancer. Surgical video or motion data localized to anatomy through imaging studies may be used to identify activities or events that increase the risk of cancer cell seeding (and subsequent metastasis). How to ensure ethical and legal guidance? (goals 4. 8/ 4. 9) With the face of data-driven clinical practice about to change in a vast manner, unprecedented ethical and legal questions pertaining to both the regulation of medical AI as well as its practical use will be raised. Moving forward, liability and medical negligence/insurance regulations need to be adapted for data-driven clinical practice.
An ethical and human rights-based framework intended to guide the development and use of AI was further proposed by  Fjeld et al. (2020) , taking eight key themes such as privacy, accountability, safety/security, transparency/explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values into account. Moreover, ethical and moral considerations regarding the democratization of data and/or AI model access will be necessary. In the specific context of surgery, first guidance on the ethical implications of integrating AI algorithms into surgical training workflows has recently become available  (Collins et al. , 2021). Similarly, new concepts for obtaining patient consent to data sharing that take into account the dynamics and unforeseeability of data usage in future SDS applications need to be established.
One way to go might be the introduction of a data donor card, analogously to organ donor cards, as suggested in Sec. 4. 4. Both patient-and healthcare professional-centric ethical and legal considerations are likely to have a large impact on the public perception of and trust in SDS, which needs to be boosted for higher patient demand. Above all, patient safety must be supported by the development of contemplative regulatory frameworks. In summary, a multi-pronged approach to address challenges that can catalyze rapid advances in SDS and to develop solutions to problems considered low-hanging fruit will be crucial to the future of SDS as a scientific field. The introduction of initial features that provide clear benefits can facilitate advanced changes. To this end, a compositional approach may be pursued wherein complex SDS products reuse simpler AI models that have been previously approved and adopted in clinical care.
While these problems have remained until today (see Sec. 3), the challenge of integrating the different information sources related to the entire patient pathway has meanwhile been widely acknowledged. Emerging standards like HL7 FHIR and the maturing efforts of IHE form a solid base for future developments. However, standards alone are not sufficient to solve the problem; hospitals need to make data acquisition, exchange and accessibility a requirement. HIT that enables fast deployment of tools for data acquisition, annotation and processing should be seen as a core service to enable cutting edge research. By centralizing such efforts, data pools can be maintained over the scope of many projects instead of creating isolated databases. This brings with it the need to standardize regulatory workflows.
Since many SDS applications could be beyond the capabilities of today's ML models (especially time-series data), it becomes crucial to enlist and bring together as many and highly qualified AI researchers of different backgrounds as possible. In a similar vein, clinical researchers who have worked independently on surgical process modeling and ob-jective skill assessment need to be featured more prominently in the development of SDS applications. Clinicians need to become actively involved in driving applications that will be of value to them, and common goals between clinicians and researchers need to be exploited, for instance in the shape of shared task forces. A concrete example for this can be seen in the integration of the SDS community with the videobased assessment program SAGES, where clinicians and researchers alike benefit from shared goals regarding data collection and annotation for surgical education  (Feldman et al.
iter ( Wintegral GmbH, Germany ),  Nicola Rieke  ( NVIDIA, Germany ),  Tobias Roß  ( German Cancer Research Center (DKFZ), Germany) ,  Roozbeh Shams  ( École Polytechnique de Montreal, Canada ),  Amber Simpson  ( Memorial Sloan Kettering Cancer Center, United States ),  Vinkle Srivastav  ( University of Strasbourg, France ),  Sarina Thomas  ( German Cancer Research Center (DKFZ), Germany) ,  Liset Vazquez Romaguera  ( Polytechnique Montreal, Canada ),  Tong Yu  ( University of Strasbourg, France ). We further thank  Tim Rädsch  ( German Cancer Research Center (DKFZ), Germany ) for his contribution to the data annotation section and  Alexander Jenke  ( NCT Dresden, Germany ) for his contribution to the data repository table. This work was supported by the  European Research Council (ERC)  starting grant COMBIOSCOPY under the  New Horizon Framework Programme grant agreement  [ ERC-2015-StG-637960 ]; the  Helmholtz Imaging Platform (HIP) ; the  NCT Heidelberg; BPI France  (project  CONDOR ); the  Johns Hopkins Science of Learning   Institute Research Grant ; the  National Institutes of Health [NIDCR   R01 DE025265 ,  P41 EB015902 ,  P41 EB015898 ,  R01 CA235589 ]; the  National Center For Tumor Diseases (NCT) Surgical Oncology Program ;  KARL STORZ SE & Co.
Michael's Hospital ; the  University of Toronto ; the  Grant-in-Aid for Scientific Research on Innovative Area  from  MEXT, Japan ; the National Cancer Data Ecosystem, contract number  19X037Q  under Task Order  HHSN26100071  from  NCI ; the project  ProteCT  [ BMBF 16SV8568 ]; the  German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany 's Excellence Strategy -EXC 2050/1 -Project  ID 390696704 -Cluster of Excellence "Centre for Tactile Internet  with  Human-in-the-Loop" (CeTI ); the  Canada Research Chair in Computer Integrated Surgery, Natural Sciences and Engineering Research Council of Canada ; the  ANR  with grants  ANR-16-CE33-0009  (project  DeepSurg ),  ANR-10-IAHU-02  ( IHU Strasbourg ) and  ANR-20-CHIA-0029-01  (Chair AI4ORSafety); and the  Federal Ministry of Economics and Energy  (grant number  BMWI 01MT17001C ) within the OP 4.
NET e. V. IEEE 11073-20702 syn-tactic inter-operability, IEEE 11073-20701 binding standard IEEE 11073-20702 is based on industry standard DPWS, other rare IoT Public consensus syntactic rare / widespread Collective term describing ID: NCT03125837 Data collection and creation of an ML-based CDSS to predict pain response, Genetic Observational, prospective, multi-center Apr. 2008 -Aug. 2021 1,200 Children's Hospital Medical Center, Cincinnati, Ohio, United States Data collection and creation of an ML-based CDSS to predict postoperative outcomes (mortality, Intraoperative hemo-dynamic parameters (blood pressure, heart rate), oxygen level, carbon dioxide level Observational, retrospective, single-center Jan. 2013 -Dec. 2017 35,000 Nova Scotia Health Authority Queen Elizabeth II hos-pitals, Halifax, Canada Data collection and creation of an ML-based system to compute continuous blood pressure of Postoperative hemo-dynamics (blood pres-sure) Observational, prospective, single-center May 2020 -Jan.
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ID: NCT03919370 ID: NCT04455568 to predict kidney including hemo-(Kia et al. , 2020) to structured clinical as-single-center York, United States ML-based systems riot Hospital, Lyon, SGML (ISO 8879) Telus International (Vancouver, BC, CA) Specialized computer vision & medical imaging risk of anesthesia re-ing cardiac surgery. (ECG bracelet). ML-based CDSS ological parameters single-center public of Korea ML-based CDSS transfer (ADT) events, non-randomized, Mar. 2020 tal, New York, New create and evaluate single-center unit, Edouard Her-(W3C), derived from syntactic / semantic widespread / universal Data serialization format for SuperAnnotate LLC (Sunnyvale, CA, US) airway that increases and reperfusion dur-wearable device and creation of an intraoperative physi-prospective, Mar. 2020 Hospital, Seoul, Re-Evaluation of an Admission discharge Interventional, Jun.
ID: NCT03286491 Data collection and creation of an ML-based CDSS to de-Preoperative digital photographs in dif-ferent positions and Observational, prospective, single-center May 2017 -May 2022 50,000 School of Medicine, Zhejiang University, Hangzhou, China oping ML-based systems. ID: NCT04220242 Data collection and creation of an ML-based CDSS to de-Intraoperative hemo-dynamic and cerebral oxygenation parame-Observational, prospective, single-center Dec. 2019 -Dec. 2022 10 Sahlgrenska Uni-versity Hospital, Gothenburg, Swe-Data collection and creation of an ML-based CDSS to predict postoperative cular stent placement ID: NCT04359745 ID: NCT04093908 ID: NCT03666312 outcomes after vas-Postoperative ECG Observational, prospective, multi-center Jul. 2020 -Jul. 2024 400 Taipei pseudo-progression.
patients. timepoints. Taipei City, Taiwan progression from therapy in Parkinson and late post-LT Ho Hospital, New images to distinguish Kingdom ulation (STN DBS) pre-LT, early post-LT University Shuang glioblastoma surgery London, United cleus deep brain stim-microbial flora data at Medical assess postoperative and King's College, after subthalamic nu-plication risk using Specialized computer vision & medical imaging with acute coronary samples for devel-ID: NCT04527094 ML-based CDSS to multi-center Foundation Trust dict motor response UPDRS variables multi-center burg, Netherlands transplant (LT) com-sources and their relationship General Blockchain, Inc. (San Jose, CA, US) General computer vision Samasource Impact Solutions, Inc. (San Francisco, CA, US) General computer vision Scale AI, Inc.
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2017 -Feb. 2018 400 Izmir Bozyaka Training and Re-search Hospital, surgery. ID: NCT04589884 Multi-objective data collection of colorec-Colorectal surgery videos and tissue Observational, prospective and Dec. 2019 -Dec. 2022 250 The Mater Miseri-cordiae University Data collection and creation of an ML-based CDSS to pre-dict postoperative res-Pre-and intraopera-tive EHR Observational, prospective, single-center Nov. 2020 -Aug. 2021 8,000 Seoul Seoul, Republic of Korea ID: NCT03955614 and creation of an intraoperative micro-prospective, Aug. 2021 faele, Milan, Italy ID: NCT03968094 ics. Data collection Preoperative and Observational, Sep. 2019 -275 IRCCS San Raf-management. University Hospital, postoperative pain assistance and analyt-ID: NCT04130789 National (OIRC) events in for OR workflow ICUs.
ID: NCT01318798 pecially for Apache Hadoop tronic health records with a Cogito Tech LLC (New York, NY, US) General computer vision outcome. teristics in abdominal ID: NCT04138771 ratory compromise with ML components United Kingdom including surgical after liver resection. Switzerland opioid response and morbidity, Intensive patients in surgical Data collection and Data collection and hemodynamic Postoperative hemo-Preoperative digital Interventional, Observational, Mar. 2018 -Jun. 2018 - the interconnection of Medicine, Seoul, Mayo Clinic 25,240 100 University of morphine Care Unit admission, usage intensive care non-creation of an ML-Evaluation of an ML-and creation of an requirements in length of hospital invasively, using based CDSS to pre-based CDSS to help ML-based CDSS to requiring surgery, readmission).
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ID: LOINC Regenstrief Institute at Indiana University School of Medicine (IUSM) in Indianapo-patients following car-system (Surgical In-score). intraoperative events lis + Community semantic inotropic support de-escalation in pediatric oxygen delivery in-dex) surgery navigation single-center (Clavien-Dindo tory), medication, brain stimulation prospective, erative complications (vitals and respira-health record and genetic data. ID: NCT01140724 monitor (IntelliVue vascular surgery, us-plasty (TKA), using motion NCT03175302 POSTOPERATIVE APPLICATIONS Evaluation of an ML-based CDSS for real-time vasoactive and Postoperative blood oxygenation parame-ters (the inadequate Observational, occasional / widespread widespread / rare Sep. 2016 retrospective, single-center Jan. 2021 -Mar. 2022 250 MX700, Philips).
ID: NCT04261062 ing data from a wear-data from a wearable Data collection Patient Data Manage-Observational, May 2014 -109,000 Format for data exchange and serialization, especially in REST-APIs Minnesota, United Terminology standard for lab-oratory and clinical measure-uments Inc. , Minneapolis, continued on next page mation Sciences ments, observations and doc-continued on next page Boston Children's Hospital, Boston, United States continued on next page The Technical Uni-able device (Snap40 device (unspecified). and creation of an ment System (PDMS) retrospective, Feb. 2022 versity of Munich, Monitor). Evaluation of a deep Preoperative MRI Observational, Mar. 2016 -30 Surgical Infor-predict risk of postop-ological parameters ID: NCT03800329 ID: NCT03406455 ML-based CDSS to data including physi-single-center Munich, Germany OpenIGTLink primarily supported diac surgery.
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At the second level, each programme will represent a case in which a set of activities is conducted to achieve risk reduction objectives. In order to study the three programmes and their different implementation in the centres, this study uses two methods of data collection and analysis: one qualitative and the other quantitative. The qualitative part includes a study of the documentary corpus, working seminars, observations and semistructured interviews. The quantitative component includes several heteroadministered questionnaires at different times: M0, M6, M9, M12.