Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Allan Carpenter Correspondence

Entries include a typed letter on Carpenter publishing house stationery and letters of correspondence from the Maine State Library

Uptake of hepatitis C specialist services and treatment following diagnosis by dried blood spot in Scotland

Background: Dried blood spot (DBS) testing for hepatitis C (HCV) was introduced to Scotland in 2009. This minimally invasive specimen provides an alternative to venipuncture and can overcome barriers to testing in people who inject drugs (PWID). Objectives: The objective of this study was to determine rates and predictors of: exposure to HCV, attendance at specialist clinics and anti-viral treatment initiation among the DBS tested population in Scotland. Study design: DBS testing records were deterministically linked to the Scottish HCV Clinical database prior to logistic regression analysis. Results: In the first two years of usage in Scotland, 1322 individuals were tested by DBS of which 476 were found to have an active HCV infection. Linkage analysis showed that 32% had attended a specialist clinic within 12 months of their specimen collection date and 18% had begun anti-viral therapy within 18 months of their specimen collection date. A significantly reduced likelihood of attendance at a specialist clinic was evident amongst younger individuals (<35 years), those of unknown ethnic origin and those not reporting injecting drug use as a risk factor. Conclusion: We conclude that DBS testing in non-clinical settings has the potential to increase diagnosis and, with sufficient support, treatment of HCV infection among PWID

The prevalence of hepatitis C virus among people of South Asian origin in Glasgow: results from a community based survey and laboratory surveillance

Background South Asians often present late with HCV or HBV related liver disease which could have been avoided with early diagnosis and subsequent treatment; however the prevalence of HCV/HBV among South Asians in Glasgow is not known. Accordingly, to inform the need for case finding among this group we aimed to examine the prevalence of Hepatitis C virus (HCV) among South Asians living in Glasgow. Methods A community-based survey recruited individuals at six mosques and four community centres serving the South Asian community during 2009-2010; participants had predominantly never been HCV tested. Laboratory surveillance data involving all individuals tested for HCV during 1993-2009 were examined and South Asians were identified using Nam Pehchan software. Results In the community-based survey, 2.6% of 1288 participants tested HCV-antibody positive; the prevalence ranged from 0.6% among those born in the UK to 3.1% among those born in Pakistan. The odds of testing HCV-antibody positive were significantly raised among those who had surgery in South Asia (aOR: 5.0, 95% CI: 2.0-12.3) and had either medical/dental treatment or an injection in South Asia (aOR: 2.2, 95% CI: 1.0-5.0). Of 6404 South Asians identified from laboratory surveillance data, 9.3% tested HCV positive. An estimated 38% (330/870) of HCV-infected South Asians living in Glasgow remain undiagnosed. Conclusions South Asians living in Glasgow, particularly those born outside the UK are at greater risk of HCV infection than the general population. Efforts to increase awareness and testing in this population are warranted.</p

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Developing a hypothetical implementation framework of expectations for monitoring early signs of psychosis relapse using a mobile app: qualitative study

Background: Relapse is a common experience for people diagnosed with psychosis, which is associated with increased service costs and profound personal and familial distress. EMPOWER (Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery) is a peer worker–supported digital intervention that aims to enable service users to self-monitor their mental health with the aim of encouraging self-management and the shared use of personal data to promote relapse prevention. Digital interventions have not been widely used in relapse prevention and, therefore, little is currently known about their likely implementation—both within trials and beyond. Objective: Seeking the perspectives of all relevant stakeholder groups is recommended in developing theories about implementation because this can reveal important group differences in understandings and assumptions about whether and for whom the intervention is expected to work. However, the majority of intervention implementation research has been retrospective. This study aimed to discover and theoretically frame implementation expectations in advance of testing and synthesize these data into a framework. Methods: To develop a hypothetical implementation framework, 149 mental health professionals, carers, and people diagnosed with psychosis participated in 25 focus groups in both Australia and the United Kingdom. An interview schedule informed by the normalization process theory was used to explore stakeholders’ expectations about the implementation of the EMPOWER intervention. Data were analyzed using thematic analysis and then theoretically framed using the Medical Research Council guidelines for understanding the implementation of complex interventions. Results: All groups expected that EMPOWER could be successfully implemented if the intervention generated data that were meaningful to mental health staff, carers, and service users within their unique roles. However, there were key differences between staff, carers, and service users about what facilitators and barriers that stakeholders believe exist for intervention implementation in both the cluster randomized controlled trial stage and beyond. For example, service user expectations mostly clustered around subjective user experiences, whereas staff and carers spoke more about the impact upon staff interactions with service users. Conclusions: A hypothetical implementation framework synthesized from stakeholder implementation expectations provides an opportunity to compare actual implementation data gathered during an ongoing clinical trial, giving valuable insights into the accuracy of these stakeholders’ previous expectations. This is among the first studies to assess and record implementation expectations for a newly developed digital intervention for psychosis in advance of testing in a clinical trial

Understanding implementation of a digital self-monitoring intervention for relapse prevention in psychosis: protocol for a mixed methods process evaluation

Background: Relapse is common in people who experience psychosis and is associated with many negative consequences, both societal and personal. People who relapse often exhibit changes (early warning signs [EWS]) in the period before relapse. Successful identification of EWS offers an opportunity for relapse prevention. However, several known barriers impede the use of EWS monitoring approaches. Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) is a complex digital intervention that uses a mobile app to enhance the detection and management of self-reported changes in well-being. This is currently being tested in a pilot cluster randomized controlled trial. As digital interventions have not been widely used in relapse prevention, little is known about their implementation. Process evaluation studies run in parallel to clinical trials can provide valuable data on intervention feasibility. Objective: This study aims to transparently describe the protocol for the process evaluation element of the EMPOWER trial. We will focus on the development of a process evaluation framework sensitive to the worldview of service users, mental health staff, and carers; the aims of the process evaluation itself; the proposed studies to address these aims; and a plan for integration of results from separate process evaluation studies into one overall report. Methods: The overall process evaluation will utilize mixed methods across 6 substudies. Among them, 4 will use qualitative methodologies, 1 will use a mixed methods approach, and 1 will use quantitative methodologies. Results: The results of all studies will be triangulated into an overall analysis and interpretation of key implementation lessons. EMPOWER was funded in 2016, recruitment finished in January 2018. Data analysis is currently under way and the first results are expected to be submitted for publication in December 2019. Conclusions: The findings from this study will help identify implementation facilitators and barriers to EMPOWER. These insights will inform both upscaling decisions and optimization of a definitive trial

Perspectives of patients, carers and mental health staff on early warning signs of relapse in psychosis: a qualitative investigation.

BACKGROUND: Relapse prevention strategies based on monitoring of early warning signs (EWS) are advocated for the management of psychosis. However, there has been a lack of research exploring how staff, carers and patients make sense of the utility of EWS, or how these are implemented in context. AIMS: To develop a multiperspective theory of how EWS are understood and used, which is grounded in the experiences of mental health staff, carers and patients. METHOD: Twenty-five focus groups were held across Glasgow and Melbourne (EMPOWER Trial, ISRCTN: 99559262). Participants comprised 88 mental health staff, 21 patients and 40 carers from UK and Australia (total n = 149). Data were analysed using constructivist grounded theory. RESULTS: All participants appeared to recognise EWS and acknowledged the importance of responding to EWS to support relapse prevention. However, recognition of and acting on EWS were constructed in a context of uncertainty, which appeared linked to risk appraisals that were dependent on distinct stakeholder roles and experiences. Within current relapse management, a process of weighted decision-making (where one factor was seen as more important than others) described how stakeholders weighed up the risks and consequences of relapse alongside the risks and consequences of intervention and help-seeking. CONCLUSIONS: Mental health staff, carers and patients speak about using EWS within a weighted decision-making process, which is acted out in the context of relationships that exist in current relapse management, rather than an objective response to specific signs and symptoms