A Review on Imaging Techniques and Artificial Intelligence Models for Osteoporosis Prediction.

Osteoporosis causes harmful influences on both men and women of all races. Bone mass, also referred to as "bone density," is frequently used to assess the health of bone. Humans frequently experience bone fractures as a result of trauma, accidents, metabolic bone diseases, and disorders of bone strength, which are typically led by changes in mineral composition and result in conditions like osteoporosis, osteoarthritis, osteopenia, etc. Artificial intelligence holds a lot of promise for the healthcare system. Data collection and preprocessing seem to be more essential for analysis, so bone images from different modalities, such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), are taken into consideration that help to recognize, classify, and evaluate the patterns in clinical images. This research presents a comprehensive overview of the performance of various image processing techniques and deep learning approaches used to predict osteoporosis through image segmentation, classification, and fault detection. This survey outlined the proposed domain-based deep learning model for image classification in addition to the initial findings. The outcome identifies the flaws in the existing literature's methodology and lays the way for future work in the deep learning-based image analysis model.

Multifunctional oil-produced reduced graphene oxide - Silver oxide composites with photocatalytic, antioxidant, and antibacterial activities.

Graphene-based nanomaterials that combine significant photocatalytic, antioxidant and antibacterial activity are very attractive candidates for biomedical and environmental applications. Conventional chemical synthesis routes may contaminate the resultant materials with toxic molecules, compromising their properties and limiting their use in biomedical applications. Ideally, to avoid any contamination, the nanomaterials should be synthesized from non-toxic precursors and reagents, e.g. foodstuff via a simple technology that does not rely on the use of hazardous chemicals yet produces materials of high quality. Here, we report an environmentally friendly, low cost reduced graphene oxide-silver-silver oxide nanocomposite with strong photocatalytic, antioxidant and antibacterial activity for environmental remediation. The reduced graphene oxide (FRGO) is synthesized from edible sunflower oil via a simple flame synthesis method. Next, silver nanoparticles (Ag/AgO/Ag2O) are produced by phytochemical reduction of AgNO3 using a reducing agent based on flavonoids from Coleus aromaticus (Mexican mint), also used in food industry. Thus-obtained FRGO-Ag/AgO/Ag2O composite is characterized using X-ray diffraction spectroscopy, scanning electron microscopy, fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. The degradation of anionic textile dye Methylene blue (MB) is used as a measure of photocatalytic activity of FRGO and FRGO/Ag/AgO/Ag2O, with solution pH, initial dye concentration, and quantity of the catalyst considered as influencing factors. FRGO-Ag/AgO/Ag2O composites show strong antioxidant activity, with improved radical inhibition as well as dye degradation properties when compared to pristine FRGO.

Diagnosis of Osteoporosis using modified U-net architecture with attention unit in DEXA and X-ray images.

BACKGROUND: Osteoporosis, a silent killing disease of fracture risk, is normally determined based on the bone mineral density (BMD) and T-score values measured in bone. However, development of standard algorithms for accurate segmentation and BMD measurement from X-ray images is a challenge in the medical field. OBJECTIVE: The purpose of this work is to more accurately measure BMD from X-ray images, which can overcome the limitations of the current standard technique to measure BMD using Dual Energy X-ray Absorptiometry (DEXA) such as non-availability and inaccessibility of DEXA machines in developing countries. In addition, this work also attempts to analyze the DEXA scan images for better segmentation and measurement of BMD. METHODS: This work employs a modified U-Net with Attention unit for accurate segmentation of bone region from X-Ray and DEXA images. A linear regression model is developed to compute BMD and T-score. Based on the value of T-score, the images are then classified as normal, osteopenia or osteoporosis. RESULTS: The proposed network is experimented with the two internally collected datasets namely, DEXSIT and XSITRAY, comprised of DEXA and X-ray images, respectively. The proposed method achieved an accuracy of 88% on both datasets. The Dice score on DEXSIT and XSITRAY is 0.94 and 0.92, respectively. CONCLUSION: Our modified U-Net with attention unit achieves significantly higher results in terms of Dice score and classification accuracy. The computed BMD and T-score values of the proposed method are also compared with the respective clinical reports for validation. Hence, using the digitized X-Ray images can be used to detect osteoporosis efficiently and accurately.

Dilemma in management of cervico-facial cystic hygroma.

Cervico facial cystic hygroma and tongue lymphagioma is rare representative of spectrum of lymphatic malformations. Conservative management with sclerosants alone has proven to be successful. However, sudden enlargement of these cervico facial lymphangiomas leads to catastrophic airway obstruction leading to debility in feeding and speech. Therefore, surgery is indicated in such case to prevent such a catastrophic problem. We report here the case of a 3-yearold boy with cervico facial hygroma involving the tongue. We successfully treated him with a combination of surgery and OK432 injection.

Homocystinuria with congenital/developmental cataract.

The aim of the study is to screen patients for homocystinuria with and without cataract and analyse for homocystine and methionine. Fifty-eight samples from 29 patients, i.e., plasma and urine collected after overnight fasting were analysed by the screening test for homocystine, and paper chromatography for homocystine and methionine. Out of 29 homocystinuric patients, 24 had cataract. Only one had appreciable amounts of methionine in his serum. He also had mental retardation as expected and belongs to Type I. The other types did not have methionine but had only homocystine. There was no mental retardation or ectopia lentis. So they belonged to Types II, III or IV. As there is excess methionine in Type I, with low cystine, cataract may be due to deficiency of cysteine and reduced glutathione and might be averted by suitable therapy, i.e., high cystine-low methionine diet with B6. In other types with low methionine, cataract may be due to decreased availability of amino acids for the synthesis of lens proteins; the treatment of choice should be B12, and folate with methionine.