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Palpation is a crucial tactile examination method for assessing abdominal organs and detecting conditions like tenderness, distention, masses, or fluid. It involves both light and deep palpation techniques, each serving specific diagnostic purposes. Light palpation helps identify tenderness and other surface-level indicators, while deep palpation locates and assess abdominal masses and organ boundaries. A skilled professional can gather valuable insights through palpation, including evaluating...
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DNN-Based Assistant in Laparoscopic Computer-Aided Palpation.

Tomohiro Fukuda1,2, Yoshihiro Tanaka1, Michitaka Fujiwara3

  • 1Department of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, Japan.

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Summary

This study introduces an AI algorithm to aid surgeons in detecting tumors during minimally invasive surgery by analyzing tactile sensor data. The algorithm shows detection performance comparable to human participants, enhancing computer-aided palpation.

Keywords:
deep neural networkdetection assistancelaparoscopytactile sensortumor

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Area of Science:

  • Medical Technology
  • Surgical Robotics
  • Artificial Intelligence in Medicine

Background:

  • Minimally invasive surgery (MIS) significantly limits surgeons' tactile sensory input, preventing manual palpation for intraoperative tumor detection.
  • Computer-aided palpation (CAP) offers a solution by acquiring and relaying tactile information, but effective feedback mechanisms are underdeveloped.
  • Ineffective feedback in CAP could negatively impact surgeon performance and diagnostic accuracy.

Purpose of the Study:

  • To propose and validate an assistance algorithm for intraoperative tumor detection in laparoscopic surgery using CAP.
  • To develop a deep neural network (DNN) model for real-time analysis of tactile sensor data time series.
  • To compare the algorithm's detection performance against human participants using signal detection theory.

Main Methods:

  • A deep neural network with three hidden layers was employed to analyze tactile sensor output time series.
  • Methods for real-time data input and detection criteria determination for the DNN model were proposed.
  • Validation involved using data from a psychophysical experiment where novice participants detected gastric tumor phantoms via tactile sensor feedback.

Main Results:

  • The algorithm's detection performance was evaluated using accuracy analysis and signal detection theory.
  • Two validation approaches were conducted, comparing the DNN model's performance with human participants.
  • The DNN model's detection performance was found to be not significantly different from human participants when user-specific data was included in model construction.

Conclusions:

  • The proposed assistance algorithm demonstrates feasibility for enhancing decision-making in computer-aided palpation.
  • The AI-driven approach shows potential for improving intraoperative tumor detection in laparoscopic surgery.
  • Further development of systematic feedback mechanisms is crucial for optimizing CAP systems.