Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy.

The aim of this study was to determine the effect of using video analysis software on the interrater reliability of visual assessments of gait videos in children with cerebral palsy. Two clinicians viewed the same random selection of 20 sagittal and frontal video recordings of 12 children with cerebral palsy routinely acquired during outpatient rehabilitation clinics. Both observers rated these videos in a random sequence for each lower limb using the Observational Gait Scale, once with standard video software and another with video analysis software (Dartfish(®)) which can perform angle and timing measurements. The video analysis software improved interrater agreement, measured by weighted Cohen's kappas, for the total score (κ 0.778→0.809) and all of the items that required angle and/or timing measurements (knee position mid-stance κ 0.344→0.591; hindfoot position mid-stance κ 0.160→0.346; foot contact mid-stance κ 0.700→0.854; timing of heel rise κ 0.769→0.835). The use of video analysis software is an efficient approach to improve the reliability of visual video assessments.

Video-assisted thoracic surgery diagnosis of thoracic spinal tuberculosis.

Tuberculous spondylitis is rare in economically well-developed countries. MRI is the most sensitive radiologic method of diagnosis. CT-guided fine needle aspiration can be an appropriate method for obtaining samples for culture, with positive cultures in 25 to 89% of cases. However, it can take >6 weeks for specimens to grow, and it is essential to have adequate culture and sensitivity studies for the diagnosis and treatment of mycobacterial diseases. We propose a minimally invasive diagnostic approach that ensures that adequate surgical specimens are obtained prior to initiating treatment.

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

Serrated polyps of the colon: how reproducible is their classification?

For several years, the lack of consensus on definition, nomenclature, natural history, and biology of serrated polyps (SPs) of the colon has created considerable confusion among pathologists. According to the latest WHO classification, the family of SPs comprises hyperplastic polyps (HPs), sessile serrated adenomas/polyps (SSA/Ps), and traditional serrated adenomas (TSAs). The term SSA/P with dysplasia has replaced the category of mixed hyperplastic/adenomatous polyps (MPs). The present study aimed to evaluate the reproducibility of the diagnosis of SPs based on currently available diagnostic criteria and interactive consensus development. In an initial round, H&E slides of 70 cases of SPs were circulated among participating pathologists across Europe. This round was followed by a consensus discussion on diagnostic criteria. A second round was performed on the same 70 cases using the revised criteria and definitions according to the recent WHO classification. Data were evaluated for inter-observer agreement using Kappa statistics. In the initial round, for the total of 70 cases, a fair overall kappa value of 0.318 was reached, while in the second round overall kappa value improved to moderate (kappa = 0.557; p < 0.001). Overall kappa values for each diagnostic category also significantly improved in the final round, reaching 0.977 for HP, 0.912 for SSA/P, and 0.845 for TSA (p < 0.001). The diagnostic reproducibility of SPs improves when strictly defined, standardized diagnostic criteria adopted by consensus are applied.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.