XAI Applications in Medical Imaging: A Survey of Methods and Challenges

Medical imaging plays a pivotal role in modern healthcare, aiding in the diagnosis, monitoring, and treatment of various medical conditions. With the advent of Artificial Intelligence (AI), medical imaging has witnessed remarkable advancements, promising more accurate and efficient analysis. However, the black-box nature of many AI models used in medical imaging has raised concerns regarding their interpretability and trustworthiness. In response to these challenges, Explainable AI (XAI) has emerged as a critical field, aiming to provide transparent and interpretable solutions for medical image analysis. This survey paper comprehensively explores the methods and challenges associated with XAI applications in medical imaging. The survey begins with an introduction to the significance of XAI in medical imaging, emphasizing the need for transparent and interpretable AI solutions in healthcare. We delve into the background of medical imaging in healthcare and discuss the increasing role of AI in this domain. The paper then presents a detailed survey of various XAI techniques, ranging from interpretable machine learning models to deep learning approaches with built-in interpretability and post hoc interpretation methods. Furthermore, the survey outlines a wide range of applications where XAI is making a substantial impact, including disease diagnosis and detection, medical image segmentation, radiology reports, surgical planning, and telemedicine. Real-world case studies illustrate successful applications of XAI in medical imaging. The challenges associated with implementing XAI in medical imaging are thoroughly examined, addressing issues related to data quality, ethics, regulation, clinical integration, model robustness, and human-AI interaction. The survey concludes by discussing emerging trends and future directions in the field, highlighting the ongoing efforts to enhance XAI methods for medical imaging and the critical role XAI will play in the future of healthcare. This survey paper serves as a comprehensive resource for researchers, clinicians, and policymakers interested in the integration of Explainable AI into medical imaging, providing insights into the latest methods, successful applications, and the challenges that lie ahead

Nuclei counting in microscopy images with three dimensional generative adversarial networks

Microscopy image analysis can provide substantial information for clinical study and understanding of biological structures. Two-photon microscopy is a type of fluorescence microscopy that can image deep into tissue with near-infrared excitation light. We are interested in methods that can detect and characterize nuclei in 3D fluorescence microscopy image volumes. In general, several challenges exist for counting nuclei in 3D image volumes. These include “crowding” and touching of nuclei, overlapping of nuclei, and shape and size variances of the nuclei. In this paper, a 3D nuclei counter using two different generative adversarial networks (GAN) is proposed and evaluated. Synthetic data that resembles real microscopy image is generated with a GAN and used to train another 3D GAN that counts the number of nuclei. Our approach is evaluated with respect to the number of groundtruth nuclei and compared with common ways of counting used in the biological research. Fluorescence microscopy 3D image volumes of rat kidneys are used to test our 3D nuclei counter. The accuracy results of proposed nuclei counter are compared with the ImageJ’s 3D object counter (JACoP) and the 3D watershed. Both the counting accuracy and the object-based evaluation show that the proposed technique is successful for counting nuclei in 3D

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs.

Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection

A rapid estimation of urea in adulterated milk using dry reagent strip

146-147This-article describes development and optimization of a visually evaluable dry reagent strip technique for semiquantitative estimation of urea in adulterated milk. It is based on (i) urease reacting with urea to liberate ammonia and carbon dioxide and (ii) liberated ammonia reacting with a specific chromogen to change color of the strip from light yellow to magenta, which is visible with naked eyes. The technique is versatile as (i) it is used single step-working reagent to complete the reaction within 30 s at room temperature, (ii) gives different shades of color from yellow to magenta, depending upon concentration of urea present in the milk, (iii) this strip can measure urea concentration as low as 0.1 g/L and (iv) this dry reagent strip is stable up to  one year at room temperature

The effect of Propolis and Xylitol chewing gums on salivary Streptococcus mutans count: A clinical trial

Background: Streptococcus mutans is one of the most common cariogenic microorganisms. Use of natural anticariogenic agents, such as Xylitol has been well-established in the literature. On the other hand, there is a scarcity of studies that have reported the antimicrobial potential of Propolis as an anticariogenic chewing agent; hence, the present study was designed. Aims: To evaluate and compare the anticariogenic action of two commercial chewing gums Propolis and Xylitol on the salivary S. mutans count in a group of children from Bengaluru city. Settings and Design: Clinical setting and experimental design. Materials and Methods: Thirty healthy children aged 8-11 years with decayed, missing, and filled teeth (dmft)/DMFT index score ≥3 were included in the study. Before the test, unstimulated saliva was collected. Children divided into Group I and II were given Propolis and Xylitol chewing gums respectively; to chew for 15 min. Saliva samples were then collected at 15 min (just after spitting) and after 1 h. The amount of S. mutans in saliva was evaluated using a selective media (MSAB). In addition, compliance of the two chewing gums among the children was tested with a questionnaire. Statistical Analysis Used: Student′s t-test. Results: Six samples out of 30 were excluded due to no growth. The total number of bacterial colonies was significantly reduced when compared to baseline in both the groups. Propolis gum showed statistically significant reduction in the number of colonies as compared to Xylitol. Xylitol gum was more preferred than Propolis gum by the children. Conclusions: Propolis chewing gum can be used as an anticariogenic agent in children

Albumin test strip for quick detection of albuminuria in human

496-498In this article, the dry -reagent test strip technique has been discussed for qualitative and semi-quantitative estimation of albumin in urine. The strip method developed in our laboratory is quick, simple, economical and based on indigenous technique. It is based on the principle of ‘‘Protein error’’ in which specific chromogen immobilized onto a pad reacts with albumin present in the urine and changes the colour of the strips from light yellow to bluegreen. The change in colour is visible to the naked eyes and can be compared to the colour chart for the <span style="font-size: 14.0pt;font-family:" times="" new="" roman";mso-fareast-font-family:hiddenhorzocr;="" color:black;mso-bidi-font-weight:bold"="" lang="EN-IN">estimation of total albumin concentration present in the urine sample. </span

Arsenazo III test strip for rapid detection of hardness of water

252-254In this article development and optimization of visually evaluable dry-reagent test strip technique for qualitative and semi-quantitative estimation of hardness of water has been described. The hardness of water is based on its alkaline earth content i.e. calcium and magnesium ions and their salts. The estimation of hardness of water in industry as well as in private sector is essential. Presently, calorimetric assay and test kit methods are commonly used, however, they are expensive and require trained persons to perform the test. The dry reagent strip developed in the laboratory is quick, simple and economical. It is based on specific chromogen immobilized on to a pad which reacts with calcium present in water and thereby changes the colour of the strip from purple to blackish blue. The change in colour is visible with naked eyes and can be compared with the colour chart

Gender-Based Violence Narratives in Internet-Based Conversations in Nigeria: Social Listening Study

BackgroundOvercoming gender inequities is a global priority recognized as essential for improved health and human development. Gender-based violence (GBV) is an extreme manifestation of gender inequities enacted in real-world and internet-based environments. In Nigeria, GBV has come to the forefront of attention since 2020, when a state of emergency was declared due to increased reporting of sexual violence. Understanding GBV-related social narratives is important to design public health interventions. ObjectiveWe explore how gender-related internet-based conversations in Nigeria specifically related to sexual consent (actively agreeing to sexual behavior), lack of consent, and slut-shaming (stigmatization in the form of insults based on actual or perceived sexuality and behaviors) manifest themselves and whether they changed between 2017 and 2022. Additionally, we explore what role events or social movements have in shaping gender-related narratives in Nigeria. MethodsSocial listening was carried out on 12,031 social media posts (Twitter, Facebook, forums, and blogs) and almost 2 million public searches (Google and Yahoo search engines) between April 2017 and May 2022. The data were analyzed using natural language processing to determine the most salient conversation thematic clusters, qualitatively analyze time trends in discourse, and compare data against selected key events. ResultsBetween 2017 and 2022, internet-based conversation about sexual consent increased 72,633%, from an average 3 to 2182 posts per month, while slut-shaming conversation (perpetrating or condemning) shrunk by 9%, from an average 3560 to 3253 posts per month. Thematic analysis shows conversation revolves around the objectification of women, poor comprehension of elements of sexual consent, and advocacy for public education about sexual consent. Additionally, posters created space for sexual empowerment and expressions of sex positivity, pushing back against others who weaponize posts in support of slut-shaming narrative. Time trend analysis shows a greater sense of empowerment in advocating for education around the legal age of consent for sexual activity, calling out double standards, and rejecting slut-shaming. However, analysis of emotions in social media posts shows anger was most prominent in sexual consent (n=1213, 73%) and slut-shaming (n=226, 64%) posts. Organic social movements and key events (#ArewaMeToo and #ChurchToo, the #SexforGrades scandal, and the #BBNaija television program) played a notable role in sparking discourse related to sexual consent and slut-shaming. ConclusionsSocial media narratives are significantly impacted by popular culture events, mass media programs, social movements, and micro influencers speaking out against GBV. Hashtags, media clips, and other content can be leveraged effectively to spread awareness and spark conversation around evolving gender norms. Public health practitioners and other stakeholders including policymakers, researchers, and social advocates should be prepared to capitalize on social media events and discourse to help shape the conversation in support of a normative environment that rejects GBV in all its forms