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OBJECTIVE: Myofascial pain syndrome (MPS) is one of the most common causes of chronic pain and affects a large portion of patients seen in specialty pain centers as well as primary care clinics. Diagnosis of MPS relies heavily on a clinician's ability to identify the presence of a myofascial trigger point (MTrP). Ultrasound can help, but requires the user to be experienced in ultrasound. Thus, this study investigates the use of texture features and deep learning strategies for the automatic identification of muscle with MTrPs (i.e., active and latent MTrPs) from normal (i.e., no MTrP) muscle. METHODS: Participants (n = 201) were recruited from Toronto Rehabilitation Institute, and ultrasound videos of their trapezius muscles were acquired. This new data set consists of 1344 images (248 active, 120 latent, 976 normal) collected from these videos. For texture analysis, several features were investigated with varying parameters (i.e., region of interest size, feature type and pixel pair relationships). Convolutional neural networks (CNN) were also applied to observe the performance of deep learning approaches. Performance was evaluated based on the classification accuracy, micro F1-score, sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The best CNN approach was able to differentiate between muscles with and without MTrPs better than the best texture feature approach, with F1-scores of 0.7299 and 0.7135, respectively. CONCLUSION: The results of this study reveal the challenges associated with MTrP identification and the potential and shortcomings of CNN and radiomics approaches in detail.

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The aging population challenges the health-care system with chronic diseases. Cerebrovascular diseases are important components of these chronic conditions. Stroke is the acute cessation of blood in the brain, which can lead to rapid tissue loss. Therefore, fast, accurate, and reliable automatic methods are required to facilitate stroke management. The performance of artificial intelligence (AI) methods is increasing in all domains. Vision tasks, including natural images and medical images, are particularly benefiting from the skills of AI models. The AI methods that can be applied to stroke imaging have a broad range, including classical machine learning tools such as support vector machines, random forests, logistic regression, and linear discriminant analysis, as well as deep learning models, such as convolutional neural networks, recurrent neural networks, autoencoders, and U-Net. Both tools can be applied to various aspects of stroke management, including time-to-event onset determination, stroke confirmation, large vessel occlusion detection, difusion restriction, perfusion deficit, core and penumbra identification, afected region segmentation, and functional outcome prediction. While building these AI models, maximum care should be exercised in order to reduce bias and build generalizable models. One of the most important prerequisites for building unbiased models is collecting large, diverse, and quality data that reflects the underlying population well and splitting the training and testing parts in a way that both represent a similar distribution. Explainability and trustworthiness are other important properties of machine learning models that could be widely adopted in clinical practices.

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PURPOSE: To measure the lesion size reduction in eyes with active toxoplasma retinochoroiditis during the disease course with swept-source optical coherence tomography angiography (SS-OCTA). METHODS: We retrospectively analysed the chorioretinal lesion size in a group of 14 eyes with a single active toxoplasma retinochoroiditis lesion. SS-OCTA was performed at the baseline and follow-up in all eyes. The 6 × 6 mm choriocapillaris slab images were evaluated with image analysis (MATLAB). The number of black and white pixels in a 1500-µm-diameter circle centred on each active lesion was counted at the time of baseline examination and at the first follow-up visit when the chorioretinal scar formation was noticed. RESULTS: Fourteen eyes with a single active toxoplasmosis retinochoroiditis lesion were included. Ten patients were female and three were male. The mean age was 29.1 ± 14.9 years. Active lesions were at the macula in five eyes, at the periphery in six eyes and juxtapapillary in three eyes. At the initial examination, the lesion area was observed as an area with a decreased flow signal on SS-OCTA. There was the perilesional capillary disruption in superficial and deep capillary plexi together with a diffuse capillary network attenuation and non-detectable flow signal zones in the choriocapillaris slabs. In addition to sulfamethoxazole-trimethoprim and azithromycin combination, oral corticosteroids were only co-administered in five (35%) eyes with macular involvement. The chorioretinal scar formation was observed in 4 to 16 weeks. At the time of inactivity, the original lesion was diminished in size when compared to its baseline in all study eyes (p = 0.001) with a mean black pixel reduction percentage of 21.8%. The reduction was 15.4% in eyes with macular lesion, 31.6% with peripheral lesions and 18.1% with juxtapapillary lesions (p = 0.001, p = 0.032, p = 0.028, p = 0.043, respectively). Visual acuity was correlated with black pixel reduction percentage in eyes with macular lesion (r = 0.56, p < 0.001). CONCLUSION: Healing of the active toxoplasma retinochoroiditis lesion size could be monitored with an OCTA-based image analysis technique. Interestingly, the reduction in the lesion size was lesser in the macular lesions than the peripheral and juxtapapillary lesions following the treatment and this might contribute to the poorer visual outcomes observed in eyes with macular lesions.

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The aim of this study was to report the outcome of topical brinzolamide 1% treatment on macular cystoid lesions resembling retinoschisis in 4 patients diagnosed with posterior microphthalmia. The medical records of 4 patients with a clinical diagnosis of posterior microphthalmia who had started topical brinzolamide 1% treatment were reviewed. Visual acuity, central foveal thickness, and cystoid lesion area percentage were used to evaluate treatment response. In the follow-up, there was a decrease in central foveal thicknesses and cystoid lesion area percentages in both eyes of 3 of the patients. However, 1 patient showed increases in both parameters. Visual acuity remained stable in 5 eyes and increased in 3 eyes. Topical brinzolamide treatment may have some positive effects on macular cystoid lesions in selected cases.

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