Professional roles of female urologists: A webinar-based survey of perceptions and obstacles to career development.

BACKGROUND: Urology, traditionally a maledominated specialty, keeping pace with the quickly changing gender landscape, has been characterized by waves of feminization. This study aims to understand the perspectives of women urologists on the obstacles to their career development, and the impact of such hurdles on their professional roles in urological education, practice, and leadership. METHODS: 119 female urology residents/consultants were surveyed via a webinar-based platform, covering relevant questions on domains of Academia, Mentorship, Leadership, Parenting, and Charity. Statistical analysis was done using frequency distribution based on the responses. RESULTS: 46.8% of the respondents felt that there is an under-representation of women in academia. 'Having a good mentor' was the most important factor for a novice to succeed in academia (68%). The most important trait in becoming a good leader was 'good communication skills' (35%), followed by 'visionary' (20%). The greatest challenge faced by leaders in the medical field was considered as 'time management' (31.9%). Only 21.2% of the participants felt difficulty in having a work-personal life balance, whereas 63.8% of them found it difficult only 'sometimes'. As a working parent, 'the guilt that they are not available all the time' was considered the most difficult aspect (59.5%), and 'more flexible schedule' was needed to make their lives as a working parent easier (46.8%). 34% of the respondents were affiliated with some charitable organizations. The biggest drive to do charity was their satisfaction with a noble cause (72.3%). CONCLUSIONS: Need for increased encouragement and recruitment of females into urology, and to support and nurture them in their career aspirations.

Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future.

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

A qualitative assessment of human cadavers embalmed by Thiel's method used in laparoscopic training for renal resection.

Human cadaveric tissue is the fundamental substrate for basic anatomic and surgical skills training. A qualitative assessment of the use of human cadavers preserved by Thiel's method for a British Association of Urological Surgeons-approved, advanced laparoscopic renal resection skills training course is described in the present study. Four trainees and four experienced laparoscopic surgeons participated in the course. All participants completed a five-point Likert scale satisfaction questionnaire after their training sessions. The quality of cadaveric tissue and the training session were assessed with particular emphasis placed on the ease of patient positioning, the ease of trocar placement, the preservation of tissue planes, the ease of renal pedicle dissection, and the quality of tissue preservation. All of the participants highly rated the quality of the cadaveric tissue embalmed by Thiel's method (mean scores for quality on the five-point Likert scale were 4.5 and 4.3 by the trainees and experienced laparoscopic surgeons, respectively). All of the steps of laparoscopic renal resection were rated 4.0 or more on the Likert scale by both trainees and faculty members. The initial response rates for using a human cadaver embalmed by Thiel's method as a training tool for laparoscopic nephrectomy showed encouraging results. The performance of a laparoscopic nephrectomy on a human cadaver embalmed by Thiel's method bears close resemblance to real laparoscopic nephrectomy procedures, and thus demonstrates added advantages to the previously reported models.