CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia detection.

BACKGROUND: Swift and accurate blood smear analyses are crucial for diagnosing leukemia and other hematological malignancies. However, manual leukocyte count and morphological evaluation remain time-consuming and prone to errors. Additionally, conventional image processing methods struggle to differentiate cells due to visual similarities between malignant and benign cell morphology. METHOD: In response to above challenges, we propose Coupled Transformer Convolutional Network (CoTCoNet) framework for leukemia classification. CoTCoNet integrates dual-feature extraction to capture long-range global features and fine-grained spatial patterns, facilitating the identification of complex hematological characteristics. Additionally, the framework employs a graph-based module to uncover hidden, biologically relevant features of leukocyte cells, along with a Population-based Meta-Heuristic Algorithm for feature selection and optimization. Furthermore, we introduce a novel combination of leukocyte segmentation and synthesis, which isolates relevant regions while augmenting the training dataset with realistic leukocyte samples. This strategy isolates relevant regions while augmenting the training data with realistic leukocyte samples, enhancing feature extraction, and addressing data scarcity without compromising data integrity. RESULTS: We evaluated CoTCoNet on a dataset of 16,982 annotated cells, achieving an accuracy of 0.9894 and an F1-Score of 0.9893. We tested CoTCoNet on four diverse, publicly available datasets (including those above) to assess generalizability. Results demonstrate a significant performance improvement over existing state-of-the-art approaches. CONCLUSIONS: CoTCoNet represents a significant advancement in leukemia classification, offering enhanced accuracy and efficiency compared to traditional methods. By incorporating explainable visualizations that closely align with cell annotations, the framework provides deeper insights into its decision-making process, further solidifying its potential in clinical settings.

Pushing the Limits of Minimally Invasive Spine Surgery-From Preoperative to Intraoperative to Postoperative Management.

The introduction of minimally invasive surgery ushered in a new era of spine surgery by minimizing the undue iatrogenic injury, recovery time, and blood loss, among other complications, of traditional open procedures. Over time, technological advancements have further refined the care of the operative minimally invasive spine patient. Moreover, pre-, and postoperative care have also undergone significant change by way of artificial intelligence risk stratification, advanced imaging for surgical planning and patient selection, postoperative recovery pathways, and digital health solutions. Despite these advancements, challenges persist necessitating ongoing research and collaboration to further optimize patient care in minimally invasive spine surgery.