PROST-Net: A Deep Learning Approach to Support Real-Time Fusion in Prostate Biopsy

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

“…Palladino et al 55 introduced PROST‐Net, a real‐time PCa segmentation model using a deep structured approach. Operating on diverse image modalities, including MRI and ultrasound, PROST‐Net processed data efficiently and exhibited competitive outcomes.…”

“…Utilizing datasets of varying sizes and characteristics, including both public datasets and private datasets, these papers emphasize achieving high diagnostic accuracy in PCa detection tasks. Further, papers 34–64 focus on segmentation accuracy, evaluating methods using metrics like DSC, Hausdorff Distance (HD), or Intersection over Union (IoU). Additionally, they compare segmentation results with existing methods, highlighting improvements in accuracy, efficiency, or robustness, while emphasizing clinical implications for treatment planning and patient outcomes.…”

Artificial intelligence (AI) for minimally invasive robotics

“…[2,3] In case of needle guidance robots (Figure 1), AI based algorithms can analyze real-time data from various sources such as imaging and sensor systems to enhance the accuracy and precision of needle placement, reducing the risk of complications and increasing the success rate of the procedure. [4] Additionally, AI algorithms can learn from previous procedures and adapt to new patientspecific conditions, enabling personalized and optimized needle placement. [5] The integration of AI algorithms can also enable the robots to make real-time decisions based on the analyzed data, optimizing the surgical outcome.…”