Reinforcement learning in surgery

Datta, Shounak; Li, Yanjun; Ruppert, Matthew M.; Ren, Yuanfang; Shickel, Benjamin; Ozrazgat‐Baslanti, Tezcan; Rashidi, Parisa

doi:10.1016/j.surg.2020.11.040

Cited by 28 publications

(20 citation statements)

References 10 publications

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“…There are various other clinical applications of reinforcement learning including diagnosis, medical imaging, and decision support tools (see refs. 2–5 and the references therein).…”

Section: Clinical Applicationsmentioning

confidence: 98%

“…A prominent clinical application of reinforcement learning is for treatment recommendation, which has been studied across a variety of diseases and treatments including radiation and chemotherapy for cancer, brain stimulation for epilepsy, and treatment strategies for sepsis. [2][3][4][5] In such treatment recommendation settings, a policy is commonly known as a dynamic treatment regime. There are various other clinical applications of reinforcement learning including diagnosis, medical imaging, and decision support tools (see refs.…”

Section: Clinical Applicationsmentioning

confidence: 99%

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Reinforcement Learning for Clinical Applications

Khezeli

Siegel

Shickel

et al. 2023

CJASN

Self Cite

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“…There are various other clinical applications of reinforcement learning including diagnosis, medical imaging, and decision support tools (see refs. 2–5 and the references therein).…”

Section: Clinical Applicationsmentioning

confidence: 98%

Section: Clinical Applicationsmentioning

confidence: 99%

Reinforcement Learning for Clinical Applications

Khezeli

Siegel

Shickel

et al. 2023

CJASN

Self Cite

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“…Reinforcement learning (RL) is a branch of ML that uses Markov Decision Processes (MDP), which are based on Markov chains, named after Andrey Markov, a Russian mathematician who used this framework to help predict the outcomes of random events [ 78 , 79 ]. In addition to AI in medicine, it has also been applied to autonomous actions in surgery [ 80 ]. As noted, enabling the computer to see and recognize things was, in the past, a great hindrance to advancements in AI.…”

Section: Deep Learning and Computer Visionmentioning

confidence: 99%

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

Gumbs

Frigerio

Spolverato

et al. 2021

Sensors

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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.

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“…In the past decade, however, deep learning has garnered signi cant achievements in the healthcare domain to facilitate the clinical decision-making process with its superior capability to detect the intricate patterns inherent in raw clinical data and to approximate highly complex functions. [12][13][14] Although several advanced deep learning algorithms have been developed to manage the irregularly-sampled time series data, [15][16][17] there remains a dearth of work speci cally focused on the clinical phenotype identi cation, particularly using the early stages vital sign data.…”

Section: Introductionmentioning

confidence: 99%

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

Ren,

Li,

Loftus

et al. 2023

Preprint

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Background: While the initial few hours of a hospital admission can significantly impact a patient’s clinical trajectory, early clinical decisions often suffer due to data paucity. By using clustering analysis for patient vital signs that were recorded in the first six hours after hospital admission, unique patient phenotypes with distinct pathophysiological signatures and clinical outcomes may be revealed and support early clinical decision-making. Historically, phenotyping based on these early vital signs has proven challenging, as vital signs are typically sampled sporadically. Methods: We created a single-center, longitudinal dataset of electronic health record data for 75,762 adult patients admitted to a tertiary care center for at least six hours. We proposed a novel, deep temporal interpolation and clustering network to simultaneously extract latent representations from sparse and irregularly sampled vital sign data and derived distinct patient phenotypes within a training cohort (n=41,502). Model and hyper-parameters were selected based on a validation cohort (n=17,415). A test cohort (n=16,845) was used to analyze reproducibility and correlation with clinical biomarkers. Results: The three cohorts—training, validation, and testing—had comparable distributions of age (54-55 years), sex (55% female), race, comorbidities, and illness severity. Four distinct clusters were identified. Phenotype A (18%) had the greatest prevalence of comorbid disease with increased prevalence of prolonged respiratory insufficiency, acute kidney injury, sepsis, and long-term (three-year) mortality. Phenotypes B (33%) and C (31%) had a diffuse pattern of mild organ dysfunction. Phenotype B’s favorable short-term clinical outcomes were tempered by the second highest rate of long-term mortality. Phenotype C had favorable clinical outcomes. Phenotype D (17%) exhibited early and persistent hypotension, high incidence of early surgery, and substantial biomarker incidence of inflammation. Despite early and severe illness, phenotype D had the second lowest long-term mortality. After comparing the various phenotypes’ sequential organ failure assessment scores, the results of the clustering did not simply provide a recapitulation of previous acuity assessments. Conclusions: Within a heterogeneous cohort of patients in hospitals, four phenotypes with distinct categories of disease and clinical outcomes were identified by using a deep temporal interpolation and clustering network. This tool may impact triage decisions and have significant implications for clinical decision-support under time constraints and uncertainty.

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Reinforcement learning in surgery

Cited by 28 publications

References 10 publications

Reinforcement Learning for Clinical Applications

Reinforcement Learning for Clinical Applications

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

Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures

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