Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.Comment: Accepted at MICCAI 201

Surgical Skill Assessment on In-Vivo Clinical Data via the Clearness of Operating Field

Surgical skill assessment is important for surgery training and quality control. Prior works on this task largely focus on basic surgical tasks such as suturing and knot tying performed in simulation settings. In contrast, surgical skill assessment is studied in this paper on a real clinical dataset, which consists of fifty-seven in-vivo laparoscopic surgeries and corresponding skill scores annotated by six surgeons. From analyses on this dataset, the clearness of operating field (COF) is identified as a good proxy for overall surgical skills, given its strong correlation with overall skills and high inter-annotator consistency. Then an objective and automated framework based on neural network is proposed to predict surgical skills through the proxy of COF. The neural network is jointly trained with a supervised regression loss and an unsupervised rank loss. In experiments, the proposed method achieves 0.55 Spearman's correlation with the ground truth of overall technical skill, which is even comparable with the human performance of junior surgeons.Comment: MICCAI 201

Results and Outcome Reporting In ClinicalTrials.gov, What Makes it Happen?

At the end of the past century there were multiple concerns regarding lack of transparency in the conduct of clinical trials as well as some ethical and scientific issues affecting the trials' design and reporting. In 2000 ClinicalTrials.gov data repository was developed and deployed to serve public and scientific communities with valid data on clinical trials. Later in order to increase deposited data completeness and transparency of medical research a set of restrains had been imposed making the results deposition compulsory for multiple cases.We investigated efficiency of the results deposition and outcome reporting as well as what factors make positive impact on providing information of interest and what makes it more difficult, whether efficiency depends on what kind of institution was a trial sponsor. Data from the ClinicalTrials.gov repository has been classified based on what kind of institution a trial sponsor was. The odds ratio was calculated for results and outcome reporting by different sponsors' class.As of 01/01/2012 118,602 clinical trials data deposits were made to the depository. They came from 9068 different sources. 35344 (29.8%) of them are assigned as FDA regulated and 25151 (21.2%) as Section 801 controlled substances. Despite multiple regulatory requirements, only about 35% of trials had clinical study results deposited, the maximum 55.56% of trials with the results, was observed for trials completed in 2008.The most positive impact on depositing results, the imposed restrains made for hospitals and clinics. Health care companies showed much higher efficiency than other investigated classes both in higher fraction of trials with results and in providing at least one outcome for their trials. They also more often than others deposit results when it is not strictly required, particularly, in the case of non-interventional studies

Evaluation of Magnetic Micro- and Nanoparticle Toxicity to Ocular Tissues

Purpose: Magnetic nanoparticles (MNPs) may be used for focal delivery of plasmids, drugs, cells, and other applications. Here we ask whether such particles are toxic to ocular structures. Methods: To evaluate the ocular toxicity of MNPs, we asked if either 50 nm or 4 mm magnetic particles affect intraocular pressure, corneal endothelial cell count, retinal morphology including both cell counts and glial activation, or photoreceptor function at different time points after injection. Sprague-Dawley rats (n = 44) were injected in the left eye with either 50 nm (3 ml, 1.65 mg) or 4 mm(3ml, 1.69 mg) magnetic particles, and an equal volume of PBS into the right eye. Electroretinograms (ERG) were used to determine if MNPs induce functional changes to the photoreceptor layers. Enucleated eyes were sectioned for histology and immunofluorescence. Results: Compared to control-injected eyes, MNPs did not alter IOP measurements. ERG amplitudes for a-waves were in the 100–250 mV range and b-waves were in the 500–600 mV range, with no significant differences between injected and noninjected eyes. Histological sectioning and immunofluorescence staining showed little difference in MNP-injected animals compared to control eyes. In contrast, at 1 week, corneal endothelial cell numbers were significantly lower in the 4 mm magnetic particle-injected eyes compared to either 50 nm MNP- or PBS-injected eyes. Furthermore, iron deposition was detected after 4 mm magnetic particle but not 50 nm MNP injection

Comparative Effectiveness Research: An Empirical Study of Trials Registered in ClinicalTrials.gov

Background The $1.1 billion investment in comparative effectiveness research will reshape the evidence-base supporting decisions about treatment effectiveness, safety, and cost. Defining the current prevalence and characteristics of comparative effectiveness (CE) research will enable future assessments of the impact of this program. Methods We conducted an observational study of clinical trials addressing priority research topics defined by the Institute of Medicine and conducted in the US between 2007 and 2010. Trials were identified in ClinicalTrials.gov. Main outcome measures were the prevalence of comparative effectiveness research, nature of comparators selected, funding sources, and impact of these factors on results. Results 231 (22.3%; 95% CI 19.8%–24.9%) studies were CE studies and 804 (77.7%; 95% CI, 75.1%–80.2%) were non-CE studies, with 379 (36.6%; 95% CI, 33.7%–39.6%) employing a placebo control and 425 (41.1%; 95% CI, 38.1%–44.1%) no control. The most common treatments examined in CE studies were drug interventions (37.2%), behavioral interventions (28.6%), and procedures (15.6%). Study findings were favorable for the experimental treatment in 34.8% of CE studies and greater than twice as many (78.6%) non-CE studies (P<0.001). CE studies were more likely to receive government funding (P = 0.003) and less likely to receive industry funding (P = 0.01), with 71.8% of CE studies primarily funded by a noncommercial source. The types of interventions studied differed based on funding source, with 95.4% of industry trials studying a drug or device. In addition, industry-funded CE studies were associated with the fewest pediatric subjects (P<0.001), the largest anticipated sample size (P<0.001), and the shortest study duration (P<0.001). Conclusions In this sample of studies examining high priority areas for CE research, less than a quarter are CE studies and the majority is supported by government and nonprofits. The low prevalence of CE research exists across CE studies with a broad array of interventions and characteristics.National Library of Medicine (U.S.) (5G08LM009778)National Institutes of Health (U.S.

Patients' knowledge and perception on optic neuritis management before and after an information session

<p>Abstract</p> <p>Background</p> <p>Patients' understanding of their condition affect the choice of treatment. The aim of this study is to evaluate patients' understanding and treatment preferences before and after an information session on the treatment of acute optic neuritis.</p> <p>Methods</p> <p>Participants were asked to complete a questionnaire consisting of 14 questions before and after an information session presented by a neuro-ophthalmologist. The information session highlighted the treatment options and the treatment effects based on the Optic Neuritis Treatment Trial in plain patient language. The information session stressed the finding that high dose intravenous steroid therapy accelerated visual recovery but does not change final vision and that treatment with oral prednisone alone resulted in a higher incidence of recurrent optic neuritis.</p> <p>Results</p> <p>Before the information session, 23 (85%) participants knew that there was treatment available for ON and this increased to 27 (100%) after the information session. There were no significantly change in patients knowledge of symptoms of ON and purpose of treatment before and after the information session. Before the information session, 4 (14%) respondents reported they would like to be treated by oral steroid alone in the event of an optic neuritis and 5 (19%) did not respond. After the education session, only 1 patient (4%) indicated they would undergo treatment with oral steroid alone but 25 (92%) indicated they would undergo treatment with intravenous steroid treatment, alone or in combination with oral treatment. Results indicated that there were significant differences in the numbers of participants selecting that they would undergo treatment with a steroid injection (n = 22, p = 0.016).</p> <p>Conclusions</p> <p>In this study, patients have shown good understanding of the symptoms and signs of optic neuritis. The finding that significant increases in the likelihood of patients engaging in best practice can be achieved with an information session is very important. This suggests that patient knowledge of available treatments and outcomes can play an important role in implementing and adopting guideline recommendations.</p

Impact of Reporting Bias in Network Meta-Analysis of Antidepressant Placebo-Controlled Trials

BACKGROUND: Indirect comparisons of competing treatments by network meta-analysis (NMA) are increasingly in use. Reporting bias has received little attention in this context. We aimed to assess the impact of such bias in NMAs. METHODS: We used data from 74 FDA-registered placebo-controlled trials of 12 antidepressants and their 51 matching publications. For each dataset, NMA was used to estimate the effect sizes for 66 possible pair-wise comparisons of these drugs, the probabilities of being the best drug and ranking the drugs. To assess the impact of reporting bias, we compared the NMA results for the 51 published trials and those for the 74 FDA-registered trials. To assess how reporting bias affecting only one drug may affect the ranking of all drugs, we performed 12 different NMAs for hypothetical analysis. For each of these NMAs, we used published data for one drug and FDA data for the 11 other drugs. FINDINGS: Pair-wise effect sizes for drugs derived from the NMA of published data and those from the NMA of FDA data differed in absolute value by at least 100% in 30 of 66 pair-wise comparisons (45%). Depending on the dataset used, the top 3 agents differed, in composition and order. When reporting bias hypothetically affected only one drug, the affected drug ranked first in 5 of the 12 NMAs but second (n = 2), fourth (n = 1) or eighth (n = 2) in the NMA of the complete FDA network. CONCLUSIONS: In this particular network, reporting bias biased NMA-based estimates of treatments efficacy and modified ranking. The reporting bias effect in NMAs may differ from that in classical meta-analyses in that reporting bias affecting only one drug may affect the ranking of all drugs

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

Why we need easy access to all data from all clinical trials and how to accomplish it

International calls for registering all trials involving humans and for sharing the results, and sometimes also the raw data and the trial protocols, have increased in recent years. Such calls have come, for example, from the Organization for Economic Cooperation and Development (OECD), the World Health Organization (WHO), the US National Institutes of Heath, the US Congress, the European Commission, the European ombudsman, journal editors, The Cochrane Collaboration, and several funders, for example the UK Medical Research Council, the Wellcome Trust, the Bill and Melinda Gates Foundation and the Hewlett Foundation