Minimal Criteria for the Diagnosis of Avulsion of the Puborectalis Muscle by Tomographic Ultrasound

Introduction and hypothesis Puborectalis avulsion is a likely etiological factor for female pelvic organ prolapse(FPOP). We performed a study to establish minimal sonographic criteria for the diagnosis of avulsion. Methods We analysed datasets of 764 women seen at a urogynecological service. Offline analysis of ultrasound datasets was performed blinded to patient data. Tomographic ultrasound imaging (TUI) was used to diagnose avulsion of the puborectalis muscle. Results Logistic regression modelling of TUI data showed that complete avulsion is best diagnosed by requiring the three central tomographic slices to be abnormal. This finding was obtained in 30% of patients and was associated with symptoms and signs of FPOP (P<0.001). Lesser degrees of trauma (‘partial avulsion’) were not associated with symptoms or signs of pelvic floor dysfunction. Conclusions Complete avulsion of the puborectalis muscle is best diagnosed on TUI by requiring all three central slices to be abnormal. Partial trauma seems of limited clinical relevance.

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.