Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications

Quality of surgery in oncology trials

Randomised controlled trials (RCTs) with surgical interventions often lack a framework to ensure surgical quality. Although recent oncology trials, such as ADDICT (D1+ vs. D2 gastrectomy), have sought to monitor surgery there has been no demonstrably reliable tool to assess surgical quality. We aimed to investigate SQA in oesophagogastric oncology trials and to develop a robust framework of consensus strategies to overcome challenges to design and implementation of SQA. A multi-method approach including both qualitative and quantitative methodologies were applied in order to address the research objectives. On systematic review of previously reported challenges to SQA in trials the most commonly encountered included: constraints of using case volume for credentialing surgeons; inter-centre variation in the definition and execution of interventions, and; insufficient monitoring of surgical quality. A meta-analysis of SQA and protocol utilisation within oesophagogastric RCTs revealed public availability of protocols and Eastern country of origin were associated with improved survival. Semi-structured interviews were subsequently conducted with expert stakeholders examining challenges to SQA in trials. Prominent mitigating strategies included operative monitoring using photographs and/or videos with a structured objective assessment tool. Expert consensus was reached for 59 strategies to overcome challenges to SQA in oncology trials. 14 (74%) of the 19 included expert stakeholder proposed strategies from chapter 4 gained consensus amongst ADDICT trial stakeholders within 2 Delphi rounds, indicating their relevance within oesophagogastric oncology RCTs. A patient focus group and survey, established to gain insight into service user perception of quality of surgery, reinforced the importance of considering operative volume and monitoring surgery using a structured methodology. Robust monitoring methods are required to assess surgical quality and oesophagectomy assessment tools were demonstrated to be reliable using generalisability theory. Condensing the expert Delphi consensus allowed formulation of a 33-item framework of strategies to overcome challenges to implementation of SQA in oncology trials (FOSQAT). Given the relevance of the expert Delphi strategies within ADDICT, in future we recommend trial committees and surgeons will not be required to conduct a Delphi process, but rather will be able to select relevant strategies for implementation from the FOSQAT consensus. Clinical validation of this framework assessing impact of implemented strategies on short and long-term outcomes should be the focus of future research in this area.Open Acces

Suitable task allocation in intelligent systems for assistive environments

The growing need of technological assistance to provide support to people with special needs demands for systems more and more efficient and with better performances. With this aim, this work tries to advance in a multirobot platform that allows the coordinated control of different agents and other elements in the environment to achieve an autonomous behavior based on the user’s needs or will. Therefore, this environment is structured according to the potentiality of each agent and elements of this environment and of the dynamic context, to generate the adequate actuation plans and the coordination of their execution.Peer ReviewedPostprint (author's final draft

Expert-augmented machine learning.

Machine learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption is limited by the level of trust afforded by given models. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of humans and machines. Here, we present expert-augmented machine learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We used a large dataset of intensive-care patient data to derive 126 decision rules that predict hospital mortality. Using an online platform, we asked 15 clinicians to assess the relative risk of the subpopulation defined by each rule compared to the total sample. We compared the clinician-assessed risk to the empirical risk and found that, while clinicians agreed with the data in most cases, there were notable exceptions where they overestimated or underestimated the true risk. Studying the rules with greatest disagreement, we identified problems with the training data, including one miscoded variable and one hidden confounder. Filtering the rules based on the extent of disagreement between clinician-assessed risk and empirical risk, we improved performance on out-of-sample data and were able to train with less data. EAML provides a platform for automated creation of problem-specific priors, which help build robust and dependable machine-learning models in critical applications