Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches.

River Mahi drains through semi-arid regions (Western India) and is a major Arabian Sea draining river. As the principal surface water source, its water quality is important to the regional population. Therefore, the river water was sampled extensively (n = 64, 16 locations, 4 seasons and 2 years) and analyzed for 11 trace elements (TEs; Sr, V, Cu, Ni, Zn, Cd, Ba, Cr, Mn, Fe, and Co). Machine learning (ML) and multivariate statistical analysis (MVSA) were applied to investigate their possible sources, spatial-temporal-annual variations, evaluate multiple water quality parameters [heavy metal pollution index (HPI), heavy metal evaluation index (HEI)], and health indices [hazard quotient (HQ), and hazard index (THI)] associated with TEs. TE levels were higher than their corresponding world average values in 100% (Sr, V and Zn), 78%(Cu), 41%(Ni), 27%(Cr), 9%(Cd), 8%(Ba), 8%(Co), 6%(Fe), and 0%(Mn), of the samples. Three principal components (PCs) accounted for 74.5% of the TE variance: PC-1 (Fe, Co, Mn and Cu) and PC-2 (Sr and Ba) are contributed from geogenic sources, while PC-3 (Cr, Ni and Zn) are derived from geogenic and anthropogenic sources. HPI, HEI, HQ and THI all indicate that water quality is good for domestic purposes and poses little hazard. ML identified Random forest as the most suitable model for predicting HEI class (accuracy: 92%, recall: 92% and precision: 94%). Even with a limited dataset, the study underscores the potential application of ML to predictive classification modeling.

Evaluation of the Effectiveness of Telemedicine in Postoperative Follow-Up Care After Dental Implant Surgery. A Pilot Study.

Aim: Telemedicine has emerged as a potential solution to enhance postoperative follow-up care after dental implant surgery, offering the convenience of remote monitoring and reduced need for in-person visits. Materials and Methods: Participants were randomly assigned to either the telemedicine group (n = 15) or the in-person group (n = 15). In the telemedicine group, patients received remote follow-up care through virtual consultations, during which they could communicate their concerns and share images of the surgical site. The in-person group received standard in-person follow-up visits. Patient satisfaction was measured using a standardized survey, with responses collected on a Likert scale. Results: Telemedicine group exhibited comparable levels of patient satisfaction (mean satisfaction score ± standard deviation: 4.6 ± 0.3) to the in-person group (4.7 ± 0.2). Moreover, clinical outcomes, including wound healing assessment, were similar between the two groups. No significant differences were observed in the incidence of postoperative complications or the need for additional interventions. Conclusion: In conclusion, this pilot study demonstrates that telemedicine is an effective alternative to traditional in-person follow-up care for postoperative dental implant surgery patients. It offers comparable patient satisfaction and clinical outcomes while proving to be more cost-effective.

Digital mammography dataset for breast cancer diagnosis research (DMID) with breast mass segmentation analysis.

Purpose:In the last two decades, computer-aided detection and diagnosis (CAD) systems have been created to help radiologists discover and diagnose lesions observed on breast imaging tests. These systems can serve as a second opinion tool for the radiologist. However, developing algorithms for identifying and diagnosing breast lesions relies heavily on mammographic datasets. Many existing databases do not consider all the needs necessary for research and study, such as mammographic masks, radiology reports, breast composition, etc. This paper aims to introduce and describe a new mammographic database. Methods:The proposed dataset comprises mammograms with several lesions, such as masses, calcifications, architectural distortions, and asymmetries. In addition, a radiologist report is provided, describing the details of the breast, such as breast density, description of abnormality present, condition of the skin, nipple and pectoral muscles, etc., for each mammogram. Results:We present results of commonly used segmentation framework trained on our proposed dataset. We used information regarding the class of abnormalities (benign or malignant) and breast tissue density provided with each mammogram to analyze the segmentation model's performance concerning these parameters. Conclusion:The presented dataset provides diverse mammogram images to develop and train models for breast cancer diagnosis applications.

Computer-Aided Breast Cancer Diagnosis: Comparative Analysis of Breast Imaging Modalities and Mammogram Repositories.

The accurate assessment or diagnosis of breast cancer depends on image acquisition and image analysis and interpretation. The expert radiologist makes image interpretation, and this process has been greatly benefited by computer technology. For image acquisition, various imaging modalities have been developed and used over the years. This research examines several imaging modalities and their associated benefits and drawbacks. Commonly used parameters such as sensitivity and specificity are also offered to evaluate the usefulness of different imaging modalities. The main focus of the research is on mammograms. Despite the availability of breast cancer datasets of imaging modalities such as MRI, ultrasounds, and thermograms, mammogram datasets are used mainly by the domain researcher. They are considered an international gold standard for the early detection of breast cancer. We discussed and analyzed widely used and publicly available mammogram repositories. We further discussed some common key constraints related to mammogram datasets to develop the deep learningbased computer-aided diagnosis (CADx) systems for breast cancer. The ideas for their improvements have also been presented.

Image Augmentation Techniques for Mammogram Analysis.

Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Case study on the use of image analysis for the simple and inexpensive colorimetric detection of Fe(iii) in water.

In the present work, we have reported a simple and cost effective colorimetric method for the detection of Fe(iii) in water. The method is based on the color change through the formation of an Fe(iii)-glycine complex at room temperature. This type of complex formation produces an intense color due to the ligand to metal charge transfer (LMCT). The rate of this type of complex formation depends appreciably on the Fe(iii) concentrations. An important aspect of the present work is that here the image analysis technique has been used successfully for the discrimination of the color obtained by the variation of the Fe(iii) concentration. The fundamental spectro-photochemical studies on the colorimetric detection of Fe(iii) by forming a metal ligand complex and thereafter the discrimination of the complex through image analysis can provide effective insight into the development of cost effective devices for the detection of liquid phase analytes.