Pelvic insufficiency fracture in an osteoporotic 47-year-old female: a case report

Insufficiency fractures (IFs) represent a form of stress fractures frequently linked to osteoporosis and a lack of vitamin D. These fractures, which are not caused by trauma, typically manifest in the pelvis and spine, although occurrences in atypical locations are also relatively frequent. The primary methods for diagnosing IF involve using plain radiographs and magnetic resonance imaging scans, which are commonly employed imaging techniques. The management involves both medical and surgical methods, tailored as per the needs of the patient. 47-year-old female patient presented to the outpatient department with complaints of low back ache with waddling gait, with pain not responding to analgesics. Laboratory and radiological assessment revealed osteoporotic Insufficiency fracture in the pelvis which was managed with both surgical and medical methods, with surgical management involving percutaneous screw fixation of the fractures. Diagnosis of the osteoporotic insufficiency fractures at atypical locations can be extremely challenging because of the inconclusive radiographs and the lack of a perceptible trauma history and hence, can be missed at the initial presentation. The management includes both operative and non-operative modalities, best tailored as per the patient needs and expectations.

Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing

Road pavement detection and segmentation are critical for developing autonomous road repair systems. However, developing an instance segmentation method that simultaneously performs multi-class defect detection and segmentation is challenging due to the textural simplicity of road pavement image, the diversity of defect geometries, and the morphological ambiguity between classes. We propose a novel end-to-end method for multi-class road defect detection and segmentation. The proposed method comprises multiple spatial and channel-wise attention blocks available to learn global representations across spatial and channel-wise dimensions. Through these attention blocks, more globally generalised representations of morphological information (spatial characteristics) of road defects and colour and depth information of images can be learned. To demonstrate the effectiveness of our framework, we conducted various ablation studies and comparisons with prior methods on a newly collected dataset annotated with nine road defect classes. The experiments show that our proposed method outperforms existing state-of-the-art methods for multi-class road defect detection and segmentation methods.Comment: Accepted to the ICRA 202

Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite their popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques.Comment: Survey paper

Road Surface Defect Detection—From Image-Based to Non-Image-Based: A Survey

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques

IMPLEMENTATION OF RAINBOW TECHNOLOGY USING GRAY SCALE

ABSTRACT Storing audio, video, text, image etc. Data on a piece of paper instead of using CD's & DVD's. With the advent in the techniques of compression and encryption it will become possible to store data equivalent to CD's or DVD's on a piece of paper in near future. After reading data we need to scale down their sampling values between 0 and 1. We will construct gray scale image from this values. So now data in the form of image can be distributed using measures like printouts. The paper can then be read through a normal scanning and the contents are decoded from matrix to reconstruct the sampled values which can be viewed or played. Though we are not able completely reconstruct noise free original data, we firmly believe that this will be important for future advancement of this idea. This extremely low cost technology will drastically reduce the cost of the storage and will provide high-speed storage as well. There are many advantages of storing data on paper such as biodegradability, cost, duplication, data transfer, speed, size, and security

Microbiological and clinical evaluation of efficacy of locally delivered tetracycline in conjunction with scaling and root planning

Introduction: Chronic periodontitis is an infectious disease which is multifactorial in etiology. The red complex bacteria have an enzyme capable of hydrolyzing the synthetic trypsin substrate, N-benzoyl-DL-arginine-2-napthylamide (BANA). Tetracycline as a bacteriostatic agent is used in the treatment of periodontitis. Objective: The aim of this study was to evaluate clinically and microbiologically the efficacy of tetracycline fibers in conjunction with scaling and root planning in chronic periodontitis patients. Methodology: A Split mouth clinical and microbiological randomized control study was done to compare the clinical effects of subgingivally delivered antimicrobial bioabsorbable controlled release 2 mg tetracycline fibers as an adjunct to scaling and root planning on one side and comparing the other side treated only with scaling and root planning only. Result: Showed both scaling and root planning and the use of tetracycline an adjunct with scaling and root planning are equally effective. Conclusion: It can be concluded that Scaling and root planing (SRP) with or without use of adjunct local drug delivery agent like tetracycline is effective in treating chronic periodontitis