An Open-source Image Analysis Toolbox for Quantitative Retinal Optical Coherence Tomography Angiography.

Background: Qualitative and quantitative assessment of retinal perfusion using optical coherence tomography angiography (OCTA) has shown to be effective in the treatment and management of various retinal and optic nerve diseases. However, manual analyses of OCTA images to calculate metrics related to Foveal Avascular Zone (FAZ) morphology, and retinal vascular density and morphology are costly, time-consuming, subject to human error, and are exposed to both inter and intra operator variability. Objective: This study aimed to develop an open-source software framework for quantitative OCTA (QOCTA). Particularly, for analyzing OCTA images and measuring several indices describing microvascular morphology, vessel morphology, and FAZ morphology. Material and Methods: In this analytical study, we developed a toolbox or QOCTA using image processing algorithms provided in MATLAB. The software automatically determines FAZ and measures several parameters related to both size and shape of FAZ including area, perimeter, Feret's diameter circularity, axial ratio, roundness, and solidity. The microvascular structure is derived from the processed image to estimate the vessel density (VD). To assess the reliability of the software, three independent operators measured the mentioned parameters for the eyes of 21 subjects. The consistency of the values was assessed using the intraclass correlation coefficient (ICC) index. Results: Excellent consistency was observed between the measurements completed for the superficial layer, ICC >0.9. For the deep layer, good reliability in the measurements was achieved, ICC >0.7. Conclusion: The developed software is reliable; hence, it can facilitate quantitative OCTA, further statistical comparison in cohort OCTA studies, and can assist with obtaining deeper insights into retinal variations in various populations.

Amplitude of Electromyographic Activity of Trunk and Lower Extremity Muscles during Oscillatory Forces of Flexi-Bar on Stable and Unstable Surfaces in People with Nonspecific Low Back Pain.

Background: Recently, the oscillatory bar has been proposed as a new and effective rehabilitation tool in people with nonspecific low back pain (NSLBP), although its effects on muscular control in this population have not been well documented, especially in lower extremity muscles and different support surface conditions. Objective: This study aimed to evaluate and compare the effects of flexi-bar use on stable and unstable surfaces on electromyographic activity of trunk and lower extremity muscles in healthy persons and those with NSLBP. Material and Methods: 18 healthy men and 18 men with NSLBP participated in this cross-sectional study. The root mean square value of electromyographic activity was calculated in the trunk and lower extremity muscles during 4 different task conditions: quiet standing (QS) or flexi-bar use on a rigid or foam support surface. A repeated measures test was used for statistical analysis. Results: The results showed that the amplitude activity of almost all muscles was significantly greater during flexi-bar use than in the QS condition (P<0.05). The rectus femoris, tibialis anterior, and gastrocnemius demands were significantly greater on the foam than the rigid surface (P<0.05). Conclusion: This study showed that oscillatory forces caused by flexi-bar use can increase muscle activation in multiple segments (hip and ankle in addition to trunk muscles) that are crucial for postural stability. Furthermore, the foam surface appeared to target the rectus femoris in addition to the ankle muscles. Using a flexi-bar may be helpful in NSLBP rehabilitation, and exercising on a foam surface may enhance additive hip muscle activity in people with NSLBP.

Assessment of macular findings by OCT angiography in patients without clinical signs of diabetic retinopathy: radiomics features for early screening of diabetic retinopathy.

This cross-sectional study aimed to quantitatively analyze the optical coherence tomography angiography (OCTA) images using MATLAB-based software and evaluate the initial changes in macular vascular density and the distortion of the foveal avascular zone (FAZ), before the clinical appearance of diabetic retinopathy. For this purpose, 21 diabetic patients without any clinical features indicating DR, and 21 healthy individuals matched with patients based on their demographic characteristics were included. Macular thickness, macular vascular density, and morphological changes of FAZ were assessed using OCTA. The diagnostic ability of morphological parameters was evaluated by receiver operating curve analysis. The intraclass correlation coefficient (ICCC) index was used to check the consistency of the extracted values. There was no significant difference in age, gender, LogMAR visual acuity, spherical equivalent, and intra-ocular pressure amongst patients and controls. No correlation was found between age and the FAZ area as well as vascular density. The vascular structure of the superficial layer showed FAZ enlargement, reduced vascular density in the macular area, and significant deviations of FAZ shape parameters (convexity and Frequency Domain Irregularity) in patients compared with healthy individuals. Measurements were highly correlated between separate imaging sessions with ICCC of over 0.85 for all parameters. The represented data suggests that radiomics parameters can be applied as both an early screening tool and guidance for better follow-up of diabetic patients who have not had any sign of DR in fundoscopic exams.

A machine learning-based system for detecting leishmaniasis in microscopic images.

BACKGROUND: Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. METHODS: We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. RESULTS: A 65% recall and 50% precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52% and 71%, respectively, related to amastigotes outside of macrophages. CONCLUSION: The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods.

A knowledge-based system for brain tumor segmentation using only 3D FLAIR images.

This study aims to develop a semi-automatic system for brain tumor segmentation in 3D MR images. For a given image, noise was corrected using SUSAN algorithm first. A specific region of interest (ROI) that contains tumor was identified and then the intensity non-uniformity in ROI was corrected via the histogram normalization and intensity scaling. Each voxel in ROI was presented using 22 features and then was categorized as tumor or non-tumor by a multiple-classifier system. T1- and T2-weighted images and fluid-attenuated inversion recovery (FLAIR) were examined. The system performance in terms of Dice index (DI), sensitivity (SE) and specificity (SP) was evaluated using 150 simulated and 30 real images from the BraTS 2012 database. The results showed that the presented system with an average DI > 0.85, SE > 0.90, and SP > 0.98 for simulated data and DI > 0.80, SE > 0.84, and SP > 0.98 for real data might be used for accurate extraction of the brain tumors. Moreover, this system is 6 times faster than a similar system that processes the whole image. In comparison with two state-of-the-art tumor segmentation methods, our system improved DI (e.g., by 0.31 for low-grade tumors) and outperformed these algorithms. Considering the costs of imaging procedures, tumor identification accuracy and computation times, the proposed system that augmented general pathological information about tumors and used only 4 features of FLAIR images can be suggested as a brain tumor segmentation system for clinical applications.

Initial Results of Bilateral Subthalamic Nucleus Stimulation for Parkinson Disease in a Newly Established Center in a Developing Country: Shiraz, Southern Iran.

OBJECTIVE: To report the establishment of a new center for deep brain stimulation (DBS) as a surgical treatment for Parkinson disease and the surgical outcomes, from 2014 to 2017 in Shiraz, Southern Iran. METHODS: A new treatment program was established in Shiraz through a multidisciplinary team in 2014. Thirty-four patients underwent implantation of subthalamic nucleus (STN) electrodes during the last 3 years. Twenty-five patients fulfilled the minimum 6-month follow-up criteria. The baseline Unified Parkinson Disease Rating Scale (UPDRS) was assessed 1 month before surgery in both off-medication and on-medication states by a movement disorder neurologist. To evaluate the outcomes, subscores of the UPDRS were assessed in all patients before surgery and at least 6 months after the operation. RESULTS: All 25 patients had advanced Parkinson disease categorized as stage 3 or 4 using the Hoehn and Yahr scale. STN DBS resulted in a dramatic improvement in motor function of most patients. A reduction in dopaminergic medication dosage (average 60% reduction) was observed. The mean improvement was 40% in UPDRS II and 67% in UPDRS III. No surgical or hardware complications were observed. Stimulation-related adverse effects, including increased falling and worsening of speech, occurred in a few patients after surgery. Most of the patients experienced weight gain after surgery. CONCLUSIONS: Bilateral STN DBS is a satisfactory and safe treatment for carefully selected patients with advanced Parkinson disease. According to the results, the procedure can be performed safely and with comparable results in developing countries around the world.