Can machine learning predict resecability of a peritoneal carcinomatosis?

Maubert, Alexandre; Birtwisle, Loïc; Bernard, Jean‐Louis; Benizri, Emmanuel; Bereder, Jean Marc

doi:10.1016/j.suronc.2019.04.008

Cited by 17 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some previous work has already used random forest for predictions. Examples are Fang et al (2010), Maubert et al (2019), and Narla and Rehkopf (2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Targeted Bidders in Government Tenders

Cappelletti

Giuffrida

2022

SSRN Journal

View full text Add to dashboard Cite

“…Some previous work has already used random forest for predictions. Examples are Fang et al (2010), Maubert et al (2019), and Narla and Rehkopf (2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Targeted Bidders in Government Tenders

Cappelletti

Giuffrida

2022

SSRN Journal

View full text Add to dashboard Cite

“…Other studies have used ML for prediction of staging [ 16 ], for treatment decision making [ 17 ], and to improve case duration estimations [ 18 ], further expanding the applicability of ML. Despite these advantages, ML presents several challenges, such as the need to process a large amount of data before it can be analyzed, the necessity of repetitively training the model, and of refining it according to the various clinical scenarios.…”

Section: Machine Learningmentioning

confidence: 99%

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Gumbs

Frigerio

Spolverato

et al. 2021

Sensors

View full text Add to dashboard Cite

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

show abstract

“…In attempts to provide better Big Data knowledge and technical expertise to operate with different data sets, databases and insight query to MDs both efficiently and cost-effectively, hospitals and HCOs often implement online modules, hire external third-party vendors or sponsor specific academic courses. For example, most MDs are familiar with diagnostic tools and electronic medical record systems, but advanced systems that use AI or data science algorithms are still an area open to much improvement [28][29][30][31]. Although these concrete needs require knowledge and data, MDs are often too busy and too tired to go through the material and retain information consciously.…”

Section: Continuous Medical Education and Learningmentioning

confidence: 99%

“…The set of color range overlay was not normalised and as we can see from the different colored scheme and legend Fig. 7 Minimum of occurrences were 2 keywords as the threshold requirement for the analysis Last but not least, AI represents one of the most promising technologies for healthcare applications, able to disrupt the system in terms of diagnosis, treatment, and follow-ups of several clinical disciplines, including oncology, radiology, and surgery [28,31,58]. Still, AI and its related technologies like machine learning and deep learning are not included in medical curricula, both undergraduate or postgraduate.…”

Section: Model Proposal For Mds' Learning Based On Big Data Research mentioning

confidence: 99%