Impact of fatigue in surgeons on performance and patient outcome: systematic review.

BACKGROUND: While fatigue is an inevitable aspect of performing surgical procedures, lack of consensus remains on its effect on surgical performance. The aim of this systematic review was to assess the effect of non-muscular fatigue on surgical outcome. METHODS: MEDLINE and Embase were searched up to 17 January 2023. Studies on students, learning, duty-hour restrictions, muscle fatigue, non-surgical or subjective outcome, the weekend effect, or time of admission were excluded. Studies were categorized based on real-life or simulated surgery. The Cochrane risk-of-bias tool was used to assess RCTs and the Newcastle-Ottawa scale was used to assess cohort studies. Due to heterogeneity among studies, data pooling was not feasible and study findings were synthesized narratively. RESULTS: From the 7251 studies identified, 134 studies (including 1 684 073 cases) were selected for analysis (110 real-life studies and 24 simulator studies). Of the simulator studies, 46% (11 studies) reported a deterioration in surgical outcome when fatigue was present, using direct measures of fatigue. In contrast, only 35.5% (39 studies) of real-life studies showed a deterioration, observed in only 12.5% of all outcome measures, specifically involving aggregated surgical outcomes. CONCLUSION: Almost half of simulator studies, along with one-third of real-life studies, consistently report negative effects of fatigue, highlighting a significant concern. The discrepancy between simulator/real-life studies may be explained by heightened motivation and effort investment in real-life studies. Currently, published fatigue and outcome measures, especially in real-life studies, are insufficient to fully define the impact of fatigue on surgical outcomes due to the absence of direct fatigue measures and crude, post-hoc outcome measures.

At some point, surgeons become tired, just like anyone else. While in other jobs, people start to perform worse as they get tired, it is not known whether this is also true for surgeons. It is important to know this because patients may be worse off if their surgeon is tired. The aim of this study was to find out if being tired affects how surgeons do their work. Medical databases were searched through for studies on tired surgeons and the impact of fatigue on their work. Some studies looked at tired surgeons during real surgery and other studies looked at tired surgeons during sessions on surgery simulators. More than 7000 studies were examined and 134 of them were selected. They included over 1.6 million surgeries. Among these studies, 110 investigated real surgeries and 24 looked at simulated surgical sessions. Interestingly, almost half of the studies looking at simulated surgeries found that being tired had a negative effect on the simulated surgery. However, in real surgeries, this happened in only one-third of studies. The difference between real surgery and simulator surgery could be because in real surgeries surgeons always try to do their best, even when fatigued, because they are dealing with real patients. Another reason could be that the tools used to check whether surgeons are tired or whether the surgery went well are not very good. To help both surgeons and patients, there is a need to find better ways to determine if surgeons are truly tired and to make sure the tests are better.

Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.

BACKGROUND: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours is one of the causes of the lack of generalizability and the suboptimal performance of deep learning (DL) based tumor auto-segmentation models. Therefore, a DL-based method was developed that outputs predicted tumor probabilities for each PET-CT voxel in the form of a probability map instead of one fixed contour. The aim of this study was to show that DL-generated probability maps for tumor segmentation are clinically relevant, intuitive, and a more suitable solution to assist radiation oncologists in gross tumor volume segmentation on PET-CT images of head and neck cancer patients. METHOD: A graphical user interface (GUI) was designed, and a prototype was developed to allow the user to interact with tumor probability maps. Furthermore, a user study was conducted where nine experts in tumor delineation interacted with the interface prototype and its functionality. The participants' experience was assessed qualitatively and quantitatively. RESULTS: The interviews with radiation oncologists revealed their preference for using a rainbow colormap to visualize tumor probability maps during contouring, which they found intuitive. They also appreciated the slider feature, which facilitated interaction by allowing the selection of threshold values to create single contours for editing and use as a starting point. Feedback on the prototype highlighted its excellent usability and positive integration into clinical workflows. CONCLUSIONS: This study shows that DL-generated tumor probability maps are explainable, transparent, intuitive and a better alternative to the single output of tumor segmentation models.