Machine learning for real-time prediction of complications induced by flexible uretero-renoscopy with laser lithotripsy

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots

Flexible Ureteroscopy Lithotripsy Operative Time Prediction Model for the Treatment of Kidney Stones

Effective time and resource management is crucial not only in the operating room but also in healthcare supply chains. Healthcare supply chains involve the movement of medical supplies, equipment, and medications from manufacturers to healthcare providers. Effective management is crucial to ensuring that patients receive the care they need promptly. In the operating room, it is essential to have an information process in place to effectively manage time and resources during the current surgical procedure. This paper focuses on developing a predictive model for the operating time of flexible ureteroscopy for kidney stones. The model can forecast surgical and preoperative time based on patient characteristics and surgeon experience. The model can assist in planning ureteroscopy procedures and preventing surgical complications, which is crucial not only for the operating room but also for healthcare supply chains. The paper presents a study that compares different feature selection methods and regression techniques. The study found that sequential backward selection combined with the extra tree regressor was the most effective approach

Transfer Learning in Keratoconus Classification

Early detection of keratoconus will provide more treatment choices, avoid heavy treatments, and help stop the rapid progression of the disease. Unlike traditional methods of keratoconus classification, this study presents a machine learning-based keratoconus classification approach, using transfer learning, applied on corneal topographic images. Classification is performed considering the three corneal classes already cited : normal, suspicious and keratoconus. Keratoconus classification is carried out using six pretrained convolutional neural networks (CNN) VGG16, InceptionV3, MobileNet, DenseNet201, Xception and EfficientNetB0. Each of these different classifiers is trained individually on five different datasets, generated from an original dataset of 2924 corneal topographic images. Original corneal topographic images have been subjected to a special preprocessing before their use by different models in the learning phase. Images of corneal maps are separated in five different datasets while removing noise and textual annotation from images. Most of models used in the classification allow good discrimination between normal cornea, suspicious and keratoconus one. Obtained results reached classification accuracy of 99.31% and 98.51% by DenseNet201 and VGG16 respectively. Obtained results indicate that transfer learning technique could well improve performance of keratoconus classification systems

DLDiagnosis: A mobile and web application for diseases classification using Deep Learning

The detection and classification of several diseases is often carried out manually by specialists in several disciplines. Consequently, the diagnosis and the follow-up of the evolution of the diseases become more delicate and slower. The objective of this paper is to propose a system, in a web and mobile modes, allowing to detect and classify several diseases, such as brain cancer and diabetic retinopathy, according to different classes by a rigorous analysis and processing of images. Proposed software classify only image-based diseases and can assist, and not replace, specialists to propose the most appropriate therapeutic strategy to the patients according to their case, it makes it possible to follow patients over time by closely following the evolution of their diseases over diagnoses

Version [1.1.0]- [DLDiagnosis: A mobile and web application for diseases classification using Deep Learning]

This paper presents version 1.1.0 of the DLDiagnosis software, which serves as an automated decision support tool for disease detection and classification through imaging analysis using deep learning. The latest update of DLDiagnosis includes significant enhancements, expanding the system to manage a total of eight different diseases. Three new diseases have been added: keratoconus, breast cancer, and pneumonia. This update focuses on refining user interface elements, identifying gaps in code logic, and improving mobile and web interface elements. Additionally, various functionalities have been improved, including reduced diagnostic latency, faster results, enhanced responsiveness, and a more consistent and intuitive diagnostic experience