The delivery of healthcare in Low-to-Medium-Income Countries (LMICs) has long posed challenges, with established models predominantly found in wealthier nations. These models are found to be either strategic or operational, and very rarely combine these two perspectives. Most importantly, these models lack a comprehensive, holistic and synchronous construct that accompanies a systems thinking approach. This research evaluates international best practices, fundamental global theories and existing systems and tools in healthcare through a systems approach. It collates these data to propose a customized systems-based, comprehensive framework for modeling and optimizing both the management and operational tiers of healthcare in LMICs. The approach is based on the adoption of digital tools, inclusive of AI, to analyze, assimilate, align and develop advanced, holistic and inclusive frameworks. The current gap in global healthcare delivery is characterized by an ongoing lack of ability to provide quality and cost-effective care, especially in the LMICs. Despite the fact that developmental challenges are unique and specific to respective countries, there are commonalities with regard to healthcare processes that present opportunities for optimization. The main challenge lies in the effective collation and synchronization of data and tools with the specific contexts of each country. This situation highlights the need for a cohesive systems approach to enhance healthcare delivery in LMICs, allowing for tailored solutions that can bridge existing gaps. This paper presents a strategic model, with initial data quantification guiding the development of the system model. The practical significance of this research lies in its potential to transform healthcare delivery in LMICs, leading to enhanced access and quality of care through optimized systems.

Purpose: The retrograde urethrogram (RUG) has been a key diagnostic tool for over a century, remaining essential despite the availability of other imaging techniques for screening, diagnosis and follow up of Urethral strictures. However, interpretation of RUG images has to be done manually and needs experience on the part of the treating urologist, which calls for a common understanding of RUGs and presents a chance to improve stricture management in a practical way. Artificial intelligence (AI) algorithms present a novel way to prevent human discrepancy while concomitantly improving the accuracy of stricture identification and classification. Methods: Dataset: We have used a balanced dataset which includes RUGs of 168 strictured cases and 178 non-strictured(healthy) cases. Task#1: The primary requirement is to identify the Urethral region in any clinically obtained RUGs and detect the presence of stricture in it. We successfully deployed a Segmentation and Classification model to categorize the whole dataset as strictured or non-strictured RUGs. Task#2: On obtaining superior accuracy, we effectively went on to identify the type of stricture based on their location, which is of clinical importance. Results: With the above-mentioned available RUG dataset from 346 cases, we could train our Deep learning model and achieve a significant accuracy of 91.53% in detection and categorizing the type of stricture. At the end, a 10-fold cross-validation yielded an accuracy of about 86.66%. Conclusion: Our attempts have successfully validated that using Deep learning (DL) tools, one could readily (i) Detect the presence of stricture in a given RUG and (ii) ultimately locate and classify these strictures effectively. Thus, these Deep learning tools could be of great clinical assistance for Urinary stricture related disease management.

Spectral clustering has gained importance in recent years due to its ability to cluster complex data as it requires only pairwise similarity among data points with its ease of implementation. The central point in spectral clustering is the process of capturing pair-wise similarity. In the literature, many research techniques have been proposed for effective construction of affinity matrix with suitable pair- wise similarity. In this paper a general framework for capturing pairwise affinity using local features such as density, proximity and structural similarity is been proposed. Topological Node Features are exploited to define the notion of density and local structure. These local features are incorporated into the construction of the affinity matrix. Experimental results, on widely used datasets such as synthetic shape datasets, UCI real datasets and MNIST handwritten datasets show that the proposed framework outperforms standard spectral clustering methods.