RESUMO
Background and objective: The integration of machine learning (ML) in health care has garnered significant attention because of its unprecedented opportunities to enhance patient care and outcomes. In this study, we trained ML algorithms for automated prediction of outcomes of ureteroscopic laser lithotripsy (URSL) on the basis of preoperative characteristics. Methods: Data were retrieved for patients treated with ureteroscopy for urolithiasis by a single experienced surgeon over a 7-yr period. Sixteen ML classification algorithms were trained to investigate correlation between preoperative characteristics and postoperative outcomes. The outcomes assessed were primary stone-free status (SFS, defined as the presence of only stone fragments <2 mm on endoscopic visualisation and at 3-mo imaging) and postoperative complications. An ensemble model was constructed from the best-performing algorithms for prediction of complications and for prediction of SFS. Simultaneous prediction of postoperative characteristics was then investigated using a multitask neural network, and explainable artificial intelligence (AI) was used to demonstrate the predictive power of the best models. Key findings and limitations: An ensemble ML model achieved accuracy of 93% and precision of 87% for prediction of SFS. Complications were mainly associated with a preoperative positive urine culture (1.44). Logistic regression revealed that SFS was impacted by the total stone burden (0.34), the presence of a preoperative stent (0.106), a positive preoperative urine culture (0.14), and stone location (0.09). Explainable AI results emphasised the key features and their contributions to the output. Conclusions and clinical implications: Technological advances are helping urologists to overcome the classic limits of ureteroscopy, namely stone size and the risk of complications. ML represents an excellent aid for correct prediction of outcomes after training on pre-existing data sets. Our ML model achieved accuracy of >90% for prediction of SFS and complications, and represents a basis for the development of an accessible predictive model for endourologists and patients in the URSL setting. Patient summary: We tested the ability of artificial intelligence to predict treatment outcomes for patients with kidney stones. We trained 16 different machine learning tools with data before surgery, such as patient age and the stone characteristics. Our final model was >90% accurate in predicting stone-free status after surgery and the occurrence of complications.
RESUMO
Presence of polyps is the root cause of colorectal cancer, hence identification of such polyps at an early stage can help in advance treatments to avoid complications to the patient. Since there are variations in the size and shape of polyps, the task of detecting them in colonoscopy images becomes challenging. Hence our work is to leverage an algorithm for segmentation and classification of the polyp of colonoscopy images using Deep learning algorithms. In this work, we propose PolypEffNetV1, a U-Net to segment the different pathologies present in the colonoscopy frame and EfficientNetB5 to classify the detected pathologies. The colonoscopy images for the segmentation process are taken from the open-source dataset KVASIR, it consists of 1000 images with "ground truth" labeling. For classification, combination of KVASIR and CVC datasets are incorporated, which consists of 1612 images with 1696 polyp regions and 760 non-polyp inflamed regions. The proposed PolypEffNetV1 produced testing accuracy of 97.1%, Jaccard index of 0.84, dice coefficient of 0.91, and F1-score of 0.89. Subsequently, for classification to evidence whether the segmented region is polyp or non-polyp inflammation, the developed classifier produced validation accuracy of 99%, specificity of 98%, and sensitivity of 99%. Hence the proposed system could be used by gastroenterologists to identify the presence of polyp in the colonoscopy images/videos which will in turn increase healthcare quality. These developed models can be either deployed on the edge of the device to enable real-time aidance or can be integrated with existing software-application for offline review and treatment planning.
