Objective: Mental imagery (MI) has long been used in learning in both fields of sports and arts. However, it is restrictively applied in surgical training according to the medical literature. Few studies have evaluated its’ feasibility and usefulness. The aim of this study is to assess the impact of mental imagery on surgical skills learning among novice’s surgeons. Material and methods: In this pilot prospective randomized comparative study; we recruited 17 residents and interns of surgery education curriculum. They were all included in their first semester of the curricula. Two groups were randomly designed. Group (a) including “Mental Imagery” volunteers (n = 9) which benefited from a mental imagery rehearsal exercise prior to physical practice, while the control group (b) (n = 8) didn’t underwent any MI process prior to surgery practice. Each participant of both groups was invited to perform an intestinal hand-sewn anastomosis on bovine intestine. Each procedure was evaluated and analyzed according to 14 qualitative criteria while each criterion was scored 0, 1 or 2 respectively assigned to the gesture was not acquired, gesture was performed with effort, or mastered gesture. The final score is 28 for those who master all 14 gestures. A non-parametric statistical comparison between the both studied groups was performed. Results: Both groups of surgery students demonstrated equivalent age, sex ratio, laterality, and surgical experience. The mean overall score is significantly higher in the MI group (a) (17.78; SD = 2.42) compared to the control group (b) (10.63, SD = 2.85). However, advanced analysis of individual assessment items showed significant statistical difference between both groups only in 6 out of 14 assessed items. Conclusion: Indeed, mental imagery will not be able to substitute the traditional learning of surgery for novice surgeons; it is an important approach for improving the technical skills acquisition and shortening the physical learning.

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications.

Background/Objectives: This research covers both tumoral and non-tumoral (adjacent normal) tissues. Non-tumoral tissue samples were obtained from surgical resection margins located at least 5 cm from the tumor edge, with histological confirmation of the absence of tumor involvement. Methods: These samples, varying from 0 weeks to 1 week, were systematically evaluated. The assessment encompassed critical histological aspects such as tissue architecture, nuclear morphology, cytoplasmic features, and membrane characteristics. A scoring system comprising three categories (good, fair, and bad) was employed to gauge the extent of morphological alterations observed in tissue specimens. Statistical analyses were conducted using the “IBM SPSS Statistics 26.0” software. Results: Our findings unveiled a statistically significant association between tissue type and morphological degradations, highlighting the impact of prolonged cold ischemia time and fixation time on cellular swelling, cellular integrity loss, and tissue architecture disruption. The correlation between normal and tumor tissue was statistically significant for pre-analytical parameters evaluated with a strong influence on tumor tissue in cold ischemia time with a p = 0.046, p = 0.020, p = 0.029. For fixation times, the impact was significant for most of the morphological parameters, p = 0.021, p = 0.005, p = 0.023. Conclusions: These observations underscore the critical importance of minimizing cold ischemia time and refining fixation protocols to uphold tissue morphology, protein and molecular integrity. Such endeavors are pivotal in ensuring accurate histopathological evaluation and facilitating precise molecular analyses in the context of digestive cancer research.