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Surgical site infection (SSI) is a growing global concern. The principal explanation for this is its adverse clinical outcomes, such as morbidity and mortality. However, the link between the economic burden of SSIs and patient outcomes needs to be sufficiently characterized. This review aims to describe the financial implications of SSIs on patient outcomes in low- and middle-income countries (LMIC). Despite the heterogeneity in study designs from multiple LMIC countries, there is a significant correlation between SSI-associated healthcare costs from increased length of stay (LOS), readmissions, reoperations, and adverse patient outcomes. This varies based on the size, degree of infection, or other patient comorbidities. SSIs are much more prevalent in LMICs. The additional financial burden incurred in managing SSIs reinforces the need to prioritize practicing interventions to prevent this complication, which resource-limited health institutions are unequipped to do and consequently have significant adverse patient outcomes., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Costabella et al.)

Multi-parameter flow cytometry (MFC) is a cornerstone in clinical decision making for leukemia and lymphoma. MFC data analysis requires manual gating of cell populations, which is time-consuming, subjective, and often limited to a two-dimensional space. In recent years, deep learning models have been successfully used to analyze data in high-dimensional space and are highly accurate. However, AI models used for disease classification with MFC data are limited to the panel they were trained on. Thus, a key challenge in deploying AI into routine diagnostics is the robustness and adaptability of such models. This study demonstrates how transfer learning can be applied to boost the performance of models with smaller datasets acquired with different MFC panels. We trained models for four additional datasets by transferring the features learned from our base model. Our workflow increased the model's overall performance and, more prominently, improved the learning rate for small training sizes., Competing Interests: H.L. is a founder and employee of res mechanica; F.E. is an employee of res mechanica; W.K. is a founder and employee of MLL; T.H. is a founder and employee of MLL., (© 2021 The Authors.)

The wealth of information captured by multiparameter flow cytometry (MFC) can be analyzed by recent methods of computer vision when represented as a single image file. We therefore transformed MFC raw data into a multicolor 2D image by a self-organizing map and classified this representation using a convolutional neural network. By this means, we built an artificial intelligence that is not only able to distinguish diseased from healthy samples, but it can also differentiate seven subtypes of mature B-cell neoplasm. We trained our model with 18,274 cases including chronic lymphocytic leukemia and its precursor monoclonal B-cell lymphocytosis, marginal zone lymphoma, mantle cell lymphoma, prolymphocytic leukemia, follicular lymphoma, hairy cell leukemia, lymphoplasmacytic lymphoma and achieved a weighted F1 score of 0.94 on a separate test set of 2,348 cases. Furthermore, we estimated the trustworthiness of a classification and could classify 70% of all cases with a confidence of 0.95 and higher. Our performance analyses indicate that particularly for rare subtypes further improvement can be expected when even more samples are available for training. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry., (© 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.)