Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives

Journal of Imaging, Journal Year: 2024, Volume and Issue: 10(10), P. 252 - 252

Published: Oct. 14, 2024

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, image analysis plays a crucial role, enabling the identification, categorization, characterization of different tissue types, cell populations, disease states within microscopic images. Historically, manual methods have been primary approach, relying expert knowledge experience pathologists to interpret samples. Early were often constrained by computational power complexity biological The advent computers digital imaging technologies challenged exclusivity human eye vision brain skills, transforming diagnostic process in these fields. increasing digitization pathological images has led application more objective efficient computer-aided techniques. Significant advancements brought about integration pathology, machine learning, advanced technologies. continuous progress learning availability data offer exciting opportunities for future. Furthermore, artificial intelligence revolutionized this field, predictive models that assist decision making. future is predicted be marked analysis. promising, will invariably lead enhanced accuracy improved prognostic predictions shape personalized treatment strategies, ultimately leading better patient outcomes.

Language: Английский

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Nutritional Assessment and Management of Patients with Brain Neoplasms Undergoing Neurosurgery: A Systematic Review

Cancers, Journal Year: 2025, Volume and Issue: 17(5), P. 764 - 764

Published: Feb. 24, 2025

Nutritional management in neurosurgical patients with brain neoplasms is critical, as optimal nutritional status potentially associated improved clinical outcomes. This systematic review aimed to analyze the impact of pre- and postoperative assessment effect prepost interventions on A was conducted using PubMed, Cochrane Library, Embase, CINAHL databases, complemented by a search grey literature. Study quality assessed Joanna Briggs Institute framework, certainty evidence graded according Oxford Centre for Evidence-Based Medicine levels. Fourteen studies, encompassing total 11,224 adult neoplasms, were included. Many these studies retrospective, had small sample sizes, examined diverse protocols. Preoperative assessment, including parameters such albumin (p < 0.001), Controlling Status score = Prognostic Index 0.010), combined oral supplements significantly Additionally, personalized counseling contributed reduction complications facilitated more effective functional recovery. care vital managing reducing enhancing recovery overall multidisciplinary team key Future research should aim standardize protocols broader applicability.

Language: Английский

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Depth determination of simulated biological tissue using X-ray radiography and feature extraction techniques: Evaluation with Bi-LSTM neural network

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(2), P. 101406 - 101406

Published: March 8, 2025

Language: Английский

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Machine Learning Model Development for Malignant Prostate Lesion Prediction Using Texture Analysis Features from Ultrasound Shear-Wave Elastography

Cancers, Journal Year: 2025, Volume and Issue: 17(8), P. 1358 - 1358

Published: April 18, 2025

Introduction: Artificial intelligence (AI) is increasingly utilized for texture analysis and the development of machine learning (ML) techniques to enhance diagnostic accuracy. ML algorithms are trained differentiate between normal malignant conditions based on provided data. Texture feature analysis, including first-order second-order features, a critical step in development. This study aimed evaluate quantitative features prostate cancer tissues identified through ultrasound B-mode shear-wave elastography (SWE) imaging develop assess models predicting classifying versus tissues. Methodology: First-order were extracted from SWE imaging, four reconstructed regions interest (ROIs) images A total 94 derived, intensity, Gray-Level Co-Occurrence Matrix (GLCM), Dependence Length (GLDLM), Run (GLRLM), Size Zone (GLSZM). Five developed evaluated using 5-fold cross-validation predict Results: Data 62 patients analyzed. All ROIs, except those derived exhibited statistically significant differences Among models, Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) demonstrated highest performance across all ROIs. These consistently achieved strong predictive accuracy Gray Pure Reconstructed Provided sensitivity specificity PCa prediction by 82%, 90%, 98%, 96%, respectively. Conclusions: with SWE-US effectively differentiates benign lesions, like contrast, entropy, correlation playing key role. Forest, SVM, Naïve showed classification performance, while grayscale reconstructions (GPSWE GRRI) enhanced detection

Language: Английский

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Enhanced boundary-directed lightweight approach for digital pathological image analysis in critical oncological diagnostics

Journal of X-Ray Science and Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Background Pathological images play a crucial role in the diagnosis of critically ill cancer patients. Since patients often seek medical assistance when their condition is severe, doctors face urgent challenge completing accurate diagnoses and developing surgical plans within limited timeframe. The complexity diversity pathological require significant investment time from specialized physicians for processing analysis, which can lead to missing optimal treatment window. Purpose Current decision support systems are challenged by high computational deep learning models, demand extensive data training, making it difficult meet real-time needs emergency diagnostics. Method This study addresses issue malignant bone tumors such as osteosarcoma proposing Lightened Boundary-enhanced Digital Image Recognition Strategy (LB-DPRS). strategy optimizes self-attention mechanism Transformer model innovatively implements boundary segmentation enhancement strategy, thereby improving recognition accuracy tissue backgrounds nuclear boundaries. Additionally, this research introduces row-column attention methods sparsify matrix, reducing burden enhancing speed. Furthermore, proposed complementary further assists convolutional layers fully extracting detailed features . Results DSC value LB-DPRS reached 0.862, IOU 0.749, params was only 10.97 M. Conclusion Experimental results demonstrate that significantly improves efficiency while maintaining prediction interpretability, providing powerful efficient osteosarcoma.

Language: Английский

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Current status and future directions of explainable artificial intelligence in medical imaging

European Journal of Radiology, Journal Year: 2024, Volume and Issue: 183, P. 111884 - 111884

Published: Dec. 7, 2024

Language: Английский

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