Artificial Intelligence in Pathology: Present and Future |Chapter 9 | Scientific Research, New Technologies and Applications Vol. 3

Artificial intelligence is the future and its use in pathology can create a tremendous impact on health care in different aspects. Its use is being initiated in the field of pathology and is on the rise with increasing acceptance. Pathology services will undergo a paradigm shift due to the implementation of computational pathology and the use of AI tools, becoming more effective and able to satisfy the demands of the precision medicine age. Moving AI models from research to clinical applications has been sluggish, not standing their success. There may be too much distance and neglect between the clinical setting and self-contained research. The merge of AI technologies into pathology has significantly impacted diagnostic precision and speed. Digital pathology platforms equipped with machine learning algorithms enable pathologists to analyze large volumes of histological images with enhanced accuracy. These systems have demonstrated remarkable capabilities in identifying subtle morphological features indicative of various diseases such as cancerous lesions or infectious conditions. Moreover, AI-driven image analysis tools can assist pathologists in differentiating between benign and malignant tumors by quantifying cellular characteristics beyond human visual perception.

 

Furthermore, AI-powered predictive models have the potential to refine prognostic assessments based on pathological findings. By leveraging vast datasets encompassing clinical outcomes and molecular profiles associated with specific diseases or tissue alterations, these algorithms can generate more tailored predictions regarding disease progression or treatment responsiveness. Through this approach, pathologists can offer more precise guidance on patient management while harnessing valuable insights from diverse sources for optimizing therapeutic intervention. The convergence of advanced image recognition techniques, virtual microscopy, and genomics data analysis could enable comprehensive profiling of individual disease phenotypes at an unprecedented level. In conclusion, AI technologies have already begun reshaping the landscape of modern pathology practices through improved diagnostic capabilities, enriched prognostic insights and envisaged pathways toward personalized healthcare delivery. The seamless integration of AI-driven solutions into daily laboratory workflows will undeniably propel pathology into a new era marked by heightened efficiency and unparalleled precision in diagnostics and therapeutic support.

 

Author (s) Details

Saqib Ahmed
Department of Pathology, Shri Guru Ram Rai Institute of Medical and Health Sciences and Hospital, Dehradun, Uttarakhand, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v3/1831

 

Sentinel Lymph Node (SLN) Metastases in Breast Carcinoma Whole Slide Image (WSI) through Densenet Deep Learning Network: An Approach towards Clinical Management and Treatment | Chapter 20 | Issues and Developments in Medicine and Medical Research Vol. 5

 This research proposes a novel sentinel lymph metastasis categorization methodology that will aid pathologists in making quick and accurate diagnoses. DenseNet-161, a CNN-based image classification model for classifying breast lymph node metastases from WSI images, was discussed in this study. Breast cancer has the intention of spreading throughout the body. Locally, cancer cells spread by infecting healthy tissue nearby. It can also spread throughout an area by infecting nearby lymph nodes, tissues, or organs. The CNN model learns the features from the training data at first. Following that, after successfully fitting the training data, it attempts to generalise and generate correct predictions for new data that it has not seen before. A model that overfits the training data is referred to as overfitting. Even after applying the thresholding pre-processing technique, the noise persists, necessitating additional pre-processing before training the model. Furthermore, data-augmentation will considerably improve the accuracy by expanding the dataset size.

Author(S) Details

Rajasekaran Subramanian
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

R. Devika Rubi
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Abhay Krishna Kasavaraju
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Samayk Jain
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Swathi Guptha
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

Suraj Raghavendra Pingali
Keshav Memorial Institute of Technology, Hyderabad, Telangana, India.

View Book:- https://stm.bookpi.org/IDMMR-V5/article/view/5913