pgs

This repository holds software used to perform analyses and generate figures/tables for the 2021 Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) Annual Meeting "Best Papers" Podium Presentation, number S107, titled: "Artificial Intelligence Prediction of Cholecystectomy Operative Course from Automated Identification of Gallbladder Inflammation". Additionally, it was published in Surgical Endoscopy in January of 2022. A link to the paper is located here.

Overview of repository contents

data/

All .csv files are comma-separated values files that are UTF-8 encoded. Values delimited by a comma. Column names are on the first line. Missing values are represented by the string NA (not surrounding by quotes). Values are quoted only if they contain a comma, quote, newline, or an NA that is a literal string NA and not a missing value.

adhesions_gt.csv

Contains the adhesions ground truth for each representative PGS image. Details on the variable (column) names below:

variable	class	description
fname	character	Filename of PGS image
labels	character	adhesions ground truth

appearance_gt.csv

Contains the appearance ground truth for each representative PGS image. Details on the variable (column) names below:

variable	class	description
fname	character	Filename of PGS image
labels	character	appearance ground truth

chole_pgs.csv

Contains the PGS, randomized surgeon ID, and various video metrics for each video. Details on the variable (column) names below:

variable	class	description
videoid	integer	Randomized sequential video ID
surgid	integer	Randomized sequential surgeon ID
pgs	integer	Parkland Grading Scale rating
time_until_1st_clip	double	Time (minutes) from start of dissection until first clip applied in Calot's Triangle
time_cvs_attained	double	Time (minutes) from start of dissection until first seen view of Critical View of Safety
laparascopic_duration	double	Duration (minutes) of laparoscopic portion of the case (Intra-operative cholangiogram time removed)
dissection_duration	double	Duration (minutes) of cystic structures' dissection
gb_removal_duration	double	Duration (minutes) of removing gallbladder from the liver bed after all cystic structures divided. Does not include prolonged hemostasis of liver bed after or during gallbladder removal
gb_hole	logical	Whether a hole was created in the gallbladder during removal from the liver bed
gb_holes	integer	Number of holes created in the gallbladder during removal from the liver bed

cv_results.csv

Contains the results of the two computer vision models and the second surgeon's annotations for PGS for representative images.

variable	class	description
name	character	Randomized UUID for PGS image
gt	integer	Ground Truth PGS
pgs_combo	integer	PGS Classifications for the Combined Adhesions/Appearance CV model
pgs_surg2	integer	PGS Classifications for the Second Surgeon
pgs_only	integer	PGS Classifications for the PGS-only CV Model
fold	integer	Cross-validation fold. Does not apply for pgs_surg2

pgs_gt.csv

Contains the PGS ground truth for each representative PGS image. Details on the variable (column) names below:

variable	class	description
fname	character	Filename of PGS image
labels	character	PGS ground truth

output/

Empty directory that will store files generated by the code.

presentation/

Directory that is used to store the graphics generated by presentation.Rmd, and the pdf file for the presentation slides, ai_pgs.pdf.

src/

cv_model_analyses.Rmd

Rmarkdown document that contains the code to analyse the CV model performance and compare to that of a second surgeon annotator. A knitted pdf that shows the code and results is also provided.

cv_model.py

Code to train and evaluate the performance of the CNN that were trained. It trains three networks. The first classifies PGS alone. The second classifies the degree of gallbladder adhesions. The third classifies gallbladder appearance. Requires CSV files generated by pgs_analyses.Rmd.

pgs_analyses.Rmd

Rmarkdown document that contains the code to analyse the effect of PGS on various outcomes. A knitted pdf that shows the code and results is also provided.

prep_folds.Rmd

Rmarkdown document that contains the code to generate the 10 folds for 10-fold cross-validation of the computer vision model. A knitted pdf that shows the code and results is also provided.

presentation.Rmd

Rmarkdown document that contain the code to generate the graphics used in the podium presention.

Questions, comments, concerns, need help?

Please contact me in the communication medium of your preference listed on my Contact page.

Citation

If you found the code helpful for your research, please cite our paper:

@article{wardArtificialIntelligencePrediction2022,
  title = {Artificial Intelligence Prediction of Cholecystectomy Operative Course from Automated Identification of Gallbladder Inflammation},
  author = {Ward, Thomas M. and Hashimoto, Daniel A. and Ban, Yutong and Rosman, Guy and Meireles, Ozanan R.},
  year = {2022},
  month = jan,
  journal = {Surgical Endoscopy},
  issn = {0930-2794, 1432-2218},
  doi = {10.1007/s00464-022-09009-z},
  langid = {english}
}

LICENSE

All code is under the ISC license:

Copyright (c) 2021 Thomas Ward thomas@thomasward.com

Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.

THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.