Agentic education · Learning science

Actionable companion · Assessment & reasoning

Learning with agency: An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Artificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves. Methods The model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos. Results The mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465–0.639) and 0.292 (range 0.0238–0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves. Conclusions In laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-024-10939-z. Keywords: Artificial intelligence (AI), Rectal cancer surgery, Nerve-like structures, Surgical complications, Surgical education

What kind of evidence is this?

Embodied learning agents · Evidence synthesis

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Surgical videos were obtained from Chiba University Hospital, Hyogo College of Medicine Hospital, and Toranomon Hospital after passing ethical reviews. Seventy surgeries performed between April 2019 and April 2023 were analysed, including robot-assisted rectal cancer operations performed using the da Vinci Xi and Xi + surgical system (Intuitive Surgical, Sunnyvale, CA, USA) and laparoscopic rectal cancer operations performed using VISERA ELITE II (Olympus, Tokyo, Japan). The recording systems (HVO-3300MT, SONY, Tokyo, Japan; AVCCAM AG-MDR15, Panasonic, Osaka, Japan) produced videos at 30 frames per second (fps). The videos were then categorised according to their use: 70 for training and validation of the algorithm and 5 for evaluation.

Limitations and uncertainty · source passage

Our results suggest the model’s potential for clinical applications; however, there are several limitations that must be considered. The accuracy of the AI model in recognising nerves can be compromised under complex intraoperative conditions such as bleeding. Overcoming this issue is critical for achieving our goal of enhancing safety by combining surgery and AI technology. We believe that focusing on collecting scenes with lower accuracy, such as bleeding scenes, from a larger set of surgical videos for training will help improve the segmentation performance. Additionally, AI models are still unapproved medical software programs, and this qualitative evaluation was not based on qualitative assessments conducted by surgeons during actual surgery. Further safety validation and regulatory approval are required, along with qualitative assessments by surgeons in clinical settings. Furthermore, the versatility of this method must be evaluated. AI models make inferences and predictions based on training datasets. NLSs are common anatomical features in many surgical areas. We previously confirmed that an algorithm trained on rectal resection datasets can recognise NLSs with significant accuracy in gastrectomy videos. The recognition of the vagus nerve in thoracic surgery areas has also been confirmed.

Design the system before the pilot

Compare the embodied interaction with an equivalent human-led or screen-based activity. Separate the effects of novelty and social presence from learning. Review physical access, sensory needs and adult supervision.

  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Kazuya Kinoshita; Tetsuro Maruyama; Nao Kobayashi; Shunsuke Imanishi; Michihiro Maruyama; Gaku Ohira; Satoshi Endo; Toru Tochigi; Mayuko Kinoshita; Yudai Fukui; Yuta Kumazu; Junji Kita; Hisashi Shinohara; Hisahiro Matsubara. An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery. Surgical Endoscopy (2024-07-29). https://doi.org/10.1007/s00464-024-10939-z

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.