mehmetkeremturkcan/SurgicalPhaseRecognition

Surgical Phase Recognition — ResNet-50

ResNet-50 models for frame-level surgical phase recognition in inguinal hernia repair videos.

GitHub: AIDL-IPAL/SurgicalPhaseRecognition

Models

Model Name	NumTrain	NumTest	Batch Size	LR	TestAcc
resnet50_108.pt	108	50	16	1.00E-05	0.8012
resnet50_202.pt	202	0	32	1.00E-05	-

resnet50_202.pt is trained on all available videos and is recommended for downstream use.

Phase Labels

The models predict 7 merged surgical phases:

0. mesh placement
1. out of body
2. peritoneal closure
3. peritoneal scoring
4. preperitoneal dissection
5. reduction of hernia
6. transitionary idle

Usage

from phaselib import initialize_model
import cv2

predictor = initialize_model(model_path="resnet50_108.pt", device="auto")

frame = cv2.imread("frame.png")
pred = predictor.predict_frame(frame)
print(pred.phase_name, pred.confidence)

See the GitHub repo for full CLI and API documentation.

Citation

@article{zang2023surgical,
  title={Surgical phase recognition in inguinal hernia repair---AI-based confirmatory baseline and exploration of competitive models},
  author={Zang, Chengbo and Turkcan, Mehmet Kerem and Narasimhan, Sanjeev and Cao, Yuqing and Yarali, Kaan and Xiang, Zixuan and Szot, Skyler and Ahmad, Feroz and Choksi, Sarah and Bitner, Daniel P and others},
  journal={Bioengineering},
  volume={10},
  number={6},
  pages={654},
  year={2023},
  publisher={MDPI}
}

@article{choksi2023bringing,
  title={Bringing Artificial Intelligence to the operating room: edge computing for real-time surgical phase recognition},
  author={Choksi, Sarah and Szot, Skyler and Zang, Chengbo and Yarali, Kaan and Cao, Yuqing and Ahmad, Feroz and Xiang, Zixuan and Bitner, Daniel P and Kostic, Zoran and Filicori, Filippo},
  journal={Surgical Endoscopy},
  volume={37},
  number={11},
  pages={8778--8784},
  year={2023},
  publisher={Springer}
}