Diabetic Macular Edema Treatment with Bevacizumab Does Not Depend on the Retinal Nonperfusion Presence.

This study evaluated the relationship between the retinal nonperfusion area (NPA) presence and the effectiveness of bevacizumab treatment (IVB) in patients with diabetic macular edema (DME). It also tested the prognostic usefulness of ultra-wide-field fluorescein angiography (UWFFA) and OptosAdvance software for diabetic retinopathy monitoring. Eighty-nine patients with DME with a macular central subfield thickness (CST) ≥ 250 µm, with (N = 49 eyes) and without (N = 49 eyes) retinal NPA, underwent nine bevacizumab injections over 12 months. NPA distribution, leakage area distribution, microaneurysm (MA) count, macular CST, diabetic retinopathy severity, and best-corrected visual acuity (BCVA) were assessed. The results show that bevacizumab reduced the macular CST from 420 to 280 µm (p < 0.001) and improved BCVA (p < 0.001) by about 10 ETDRS letters in both groups of patients. Additionally, the therapy reduced total retinal NPA from 29 (14-36) mm2 to 12 (4-18) mm2 (Me (Q1-Q3); p < 0.001) in patients with diagnosed nonperfusion. The effect of the therapy measured with vascular leakage, MA count, BCVArelative, and CSTrelative strongly depended on the zone of the retina and the NPA distribution. We conclude that the bevacizumab treatment had a positive effect on DME and BCVA in both study groups and on the size of retinal NPA in patients with retinal nonperfusion.

Target registration error reduction for percutaneous abdominal intervention.

A real-time methodology that finds spatio-temporal correspondence between the positions of the target point in the pre-treatment 3DCT image and during the procedure was proposed. It based on minimizing the target registration error in III tier registration circuits. Particle Swarm Optimization and Differential Evaluation were used to find optimal values of Elastic Body Spline parameters in the generation of abdominal deformation field. Different transformation classes have been tested: rigid, affine, Thin Plate Spline, Elastic Body Spline. The lowest TRE was obtained for the swarm optimization algorithm - differential evolution for the rigid and affine version: 3.47 and 3.73 mm, respectively.

Computer-aided therapeutic diagnosis for anorexia.

BACKGROUND: Anorexia nervosa is a clinical disorder syndrome of the wide spectrum without a fully recognized etiology. The necessary issue in the clinical diagnostic process is to detect the causes of this disease (e.g., my body image, food, family, peers), which the therapist gradually comes to by verifying assumptions using proper methods and tools for diagnostic process. When a person is diagnosed with anorexia, a clinician (a doctor, a therapist or a psychologist) proposes a therapeutic diagnosis and considers the kind of treatment that should be applied. This process is also continued during therapeutic diagnosis. In both cases, it is recommended to apply computer-aided tools designed for testing and confirming the assumptions made by a psychologist. The paper aims to present the computer-aided therapeutic diagnosis method for anorexia. The proposed method consists of 4 stages: free statements of a patient about his/her body image, the general sentiment analysis of statement based on Recurrent Neural Network, assessment of the intensity of five basic emotions: happiness, anger, sadness, fear and disgust (using the Nencki Affective Word List and conversion of words to their basic form), and the assessment of particular areas of difficulties-the sentiment analysis based on the dictionary approach was applied. RESULTS: The sentiment analysis of a document achieved 72% and 51% of effectiveness, respectively, for RNN and dictionary-based methods. The intensity of sadness (emotion) occurring within the dictionary method is differentiated between control and research group at the level of 10%. CONCLUSION: The quick access to the sentiment analysis of a statement on the image of patient's body, emotions experienced by the patient and particular areas of difficulties of people prone to the anorexia nervosa disorders, may help to establish the diagnosis in a very short time and start an immediate therapy. The proposed automatic method helps to avoid patient's aversions towards the therapy, which may include avoiding patient-therapist communication, talking about less essential topics, coming late for the sessions. These circumstances can guarantee promising prognosis for recovering.

Anisotropic non-rigid Iterative Closest Point Algorithm for respiratory motion abdominal surface matching.

Surface registration is a one of the crucial and actual problems of computer aided surgery. This paper presents the modification of the non-rigid Iterative Closest Point Algorithm which takes into account an anisotropic noise model and landmarks as guided correspondence at the transformation step in every iteration. The presented approach was validated on human abdominal briefing surface data from a time-of-flight camera. We took the median of the resulting measures and the outcome is presented: the median of means of surfaces distance was at the same level for both variants of the ICP algorithm and is comparable with the isotropic variant, the median of mean landmark position errors decreased by 0.93 units (over 20% improvement) and the median of percentage of single correspondences in target point cloud increased by 11.96%. The results showed that the introduction of the anisotropic model of noise for the ToF camera allows for the improvement the percentage of target cloud points which had only one correspondent over 10% impartment and additional weighting of markers also improves the measure of the quality of finding real correspondents over 20% improvement. In the examined dataset, where the average initial distance between the clouds of points in the inspiratory and expiration is equal to approx. 7.5 mm, a more than 10% improvement in the quality of the correspondence improves the accuracy of matching the surface within 1 mm which is a significant value in application of minimally invasive image guided interventions.