BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on pre...
Artificial intelligence (AI) has the potential to improve the surgical treatment of patients with head and neck cancer. AI algorithms can analyse a wide range of data, including images, voice, molecular expression and raw clinical data. In the field ...
BACKGROUND: Most radical prostatectomies are completed with robotic assistance. While studies have previously evaluated perioperative outcomes of robot-assisted radical prostatectomy (RARP), this study investigates disparities in access and clinical ...
BACKGROUND AND OBJECTIVES: The role of time to surgery (TTS) for long-term outcomes in colon cancer (CC) remains ill-defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact.
BACKGROUND: Minimally invasive techniques for pancreaticoduodenectomy (PD) are increasing in practice, however, data remains limited regarding perioperative outcomes. Our study sought to compare patients undergoing open pancreaticoduodenectomy (OPD) ...
OBJECTIVE: To develop machine-learning models to predict recurrence and time-to-recurrence in high-grade endometrial cancer (HGEC) following surgery and tailored adjuvant treatment.