Interobserver and intraobserver variability of the apparent diffusion coefficient in treated malignant hepatic lesions on a 3.0T machine: measurements in the whole lesion versus in the area with the most restricted diffusion.

PURPOSE: To assess the inter/intraobserver variability of apparent diffusion coefficient (ADC) measurements in treated hepatic lesions and to compare ADC measurements in the whole lesion and in the area with the most restricted diffusion (MRDA). MATERIALS AND METHODS: Twenty-five patients with treated malignant liver lesions were examined on a 3.0T machine. After agreeing on the best ADC image, two readers independently measured the ADC values in the whole lesion and in the MRDA. These measurements were repeated 1 month later. The Bland-Altman method, Spearman correlation coefficients, and the Wilcoxon signed-rank test were used to evaluate the measurements. RESULTS: Interobserver variability for ADC measurements in the whole lesion and in the MRDA was 0.17 x 10(-3) mm(2)/s [-0.17, +0.17] and 0.43 x 10(-3) mm(2)/s [-0.45, +0.41], respectively. Intraobserver limits of agreement could be as low as [-0.10, +0.12] 10(-3) mm(2)/s and [-0.20, +0.33] 10(-3) mm(2)/s for measurements in the whole lesion and in the MRDA, respectively. CONCLUSION: A limited variability in ADC measurements does exist, and it should be considered when interpreting ADC values of hepatic malignancies. This is especially true for the measurements of the minimal ADC.

Assessment of construct validity of a virtual reality laparoscopy simulator.

BACKGROUND: The aim of this study was to assess whether virtual reality (VR) can discriminate between the skills of novices and intermediate-level laparoscopic surgical trainees (construct validity), and whether the simulator assessment correlates with an expert's evaluation of performance. METHODS: Three hundred and seven (307) participants of the 19th-22nd Davos International Gastrointestinal Surgery Workshops performed the clip-and-cut task on the Xitact LS 500 VR simulator (Xitact S.A., Morges, Switzerland). According to their previous experience in laparoscopic surgery, participants were assigned to the basic course (BC) or the intermediate course (IC). Objective performance parameters recorded by the simulator were compared to the standardized assessment by the course instructors during laparoscopic pelvitrainer and conventional surgery exercises. RESULTS: IC participants performed significantly better on the VR simulator than BC participants for the task completion time as well as the economy of movement of the right instrument, not the left instrument. Participants with maximum scores in the pelvitrainer cholecystectomy task performed the VR trial significantly faster, compared to those who scored less. In the conventional surgery task, a significant difference between those who scored the maximum and those who scored less was found not only for task completion time, but also for economy of movement of the right instrument. CONCLUSIONS: VR simulation provides a valid assessment of psychomotor skills and some basic aspects of spatial skills in laparoscopic surgery. Furthermore, VR allows discrimination between trainees with different levels of experience in laparoscopic surgery establishing construct validity for the Xitact LS 500 clip-and-cut task. Virtual reality may become the gold standard to assess and monitor surgical skills in laparoscopic surgery.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Virtual autopsy with multiphase postmortem computed tomographic angiography versus traditional medical autopsy to investigate unexpected deaths of hospitalized patients: a cohort study.

BACKGROUND: "Virtual" autopsy by postmortem computed tomography (PMCT) can replace medical autopsy to a certain extent but has limitations for cardiovascular diseases. These limitations might be overcome by adding multiphase PMCT angiography. OBJECTIVE: To compare virtual autopsy by multiphase PMCT angiography with medical autopsy. DESIGN: Prospective cohort study. (ClinicalTrials.gov: NCT01541995) SETTING: Single-center study at the University Medical Center Hamburg-Eppendorf, Hamburg, Germany, between 1 April 2012 and 31 March 2013. PATIENTS: Hospitalized patients who died unexpectedly or within 48 hours of an event necessitating cardiopulmonary resuscitation. MEASUREMENTS: Diagnoses from clinical records were compared with findings from both types of autopsy. New diagnoses identified by autopsy were classified as major or minor, depending on whether they would have altered clinical management. RESULTS: Of 143 eligible patients, 50 (35%) had virtual and medical autopsy. Virtual autopsy confirmed 93% of all 336 diagnoses identified from antemortem medical records, and medical autopsy confirmed 80%. In addition, virtual and medical autopsy identified 16 new major and 238 new minor diagnoses. Seventy-three of the virtual autopsy diagnoses, including 32 cases of coronary artery stenosis, were identified solely by multiphase PMCT angiography. Of the 114 clinical diagnoses classified as cardiovascular, 110 were confirmed by virtual autopsy and 107 by medical autopsy. In 11 cases, multiphase PMCT angiography showed "unspecific filling defects," which were not reported by medical autopsy. LIMITATION: These results come from a single center with concerted interest and expertise in postmortem imaging; further studies are thus needed for generalization. CONCLUSION: In cases of unexpected death, the addition of multiphase PMCT angiography increases the value of virtual autopsy, making it a feasible alternative for quality control and identification of diagnoses traditionally made by medical autopsy. PRIMARY FUNDING SOURCE: University Medical Center Hamburg-Eppendorf.

Access to a simulator is not enough: the benefits of virtual reality training based on peer-group-derived benchmarks--a randomized controlled trial.

BACKGROUND: Virtual reality (VR) simulators are widely used to familiarize surgical novices with laparoscopy, but VR training methods differ in efficacy. In the present trial, self-controlled basic VR training (SC-training) was tested against training based on peer-group-derived benchmarks (PGD-training). METHODS: First, novice laparoscopic residents were randomized into a SC group (n = 34), and a group using PGD-benchmarks (n = 34) for basic laparoscopic training. After completing basic training, both groups performed 60 VR laparoscopic cholecystectomies for performance analysis. Primary endpoints were simulator metrics; secondary endpoints were program adherence, trainee motivation, and training efficacy. RESULTS: Altogether, 66 residents completed basic training, and 3,837 of 3,960 (96.8 %) cholecystectomies were available for analysis. Course adherence was good, with only two dropouts, both in the SC-group. The PGD-group spent more time and repetitions in basic training until the benchmarks were reached and subsequently showed better performance in the readout cholecystectomies: Median time (gallbladder extraction) showed significant differences of 520 s (IQR 354-738 s) in SC-training versus 390 s (IQR 278-536 s) in the PGD-group (p < 0.001) and 215 s (IQR 175-276 s) in experts, respectively. Path length of the right instrument also showed significant differences, again with the PGD-training group being more efficient. CONCLUSIONS: Basic VR laparoscopic training based on PGD benchmarks with external assessment is superior to SC training, resulting in higher trainee motivation and better performance in simulated laparoscopic cholecystectomies. We recommend such a basic course based on PGD benchmarks before advancing to more elaborate VR training.

Mort des voyageurs: risques réels, risques virtuels [True and virtual risks of travellers].

Evidence-based information on travel associated mortality is scarce. Perception, intuition and the availability of interventions such as vaccinations and chemoprophylaxis often guide pre-travel advice. Important risks including accidents and cardiovascular events are not routinely included in pre-travel consultations although they cause more fatalities and costs than infectious diseases. The increased risk of sustaining a road accident in poor economy countries should always be mentioned. The general practitioner is further best placed to discuss possible problems of travellers with chronic diseases before travel.

3D virtual rendering before laparoscopic partial splenectomy in children.

INTRODUCTION: Partial splenectomy in children is a good surgical option for hematological diseases and focal splenic tumors because it allows the preservation of the spleen's immunological function. Furthermore, it can be performed by laparoscopy in children as it is a safe procedure, offering the benefits of a minimally invasive approach. MATERIALS AND METHODS: The software VR-render LE version 0.81 is a system that enables the visualization of bidimentional 3D images with magnification of anatomical details. We have applied this system to five cases of non-parasitic splenic cysts before laparoscopic partial splenectomy. RESULTS: The images obtained with VR rendering software permitted the preoperative reconstruction of the vascularization of the splenic hilum, allowing the surgeon safe vessel control during laparoscopic procedures. All five partial splenectomies were carried out with no complications or major blood loss. CONCLUSIONS: Laparoscopic partial splenectomy should be a first choice procedure because it is feasible, reproducible, and safe for children; furthermore, it preserves enough splenic tissue thereby preventing post-splenectomy infections. Volume rendering provides high anatomical resolution and can be useful in guiding the surgical procedure.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.