Transoesophageal Echocardiography for Monitoring Liver Surgery: Data from a Pilot Study

A pilot study aimed to introduce intraoperative monitoring of liver surgery using transoesophageal echocardiography (TEE) is described. A set of TEE measurements was established as a protocol, consisting of left atrial (LA) dimension at the aortic valve plane; mitral velocity flow integral, calculation of stroke volume and cardiac output (CO); mitral annular plane systolic excursion; finally, right atrial area. A total of 165 measurements (on 21 patients) were performed, 31 occurring during hypotension. The conclusions reached were during acute blood loss LA dimension changed earlier than CVP, and, in one patient, a dynamic left ventricular (LV) obstruction was observed; in 3 patients a transient LV systolic dysfunction was documented. The comparison between 39 CO paired measurements obtained by TEE and PiCCO2 revealed a statistically significant correlation (P < 0.001, r = 0.83). In this pilot study TEE successfully answered the questions raised by the anesthesiologists. Larger cohort studies are needed to address this issue.

Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.