A scoping review of artificial intelligence applications in thoracic surgery

Development of a machine learning-based risk model for postoperative complications of lung cancer surgery

PURPOSE

To develop a comorbidity risk score specifically for lung resection surgeries.

METHODS

We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI).

RESULTS

The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset.

CONCLUSIONS

The new machine learning model could predict postoperative complications with acceptable accuracy.

CLINICAL REGISTRATION NUMBER

2020-0375.

Human visual explanations mitigate bias in AI-based assessment of surgeon skills

Artificial intelligence (AI) systems can now reliably assess surgeon skills through videos of intraoperative surgical activity. With such systems informing future high-stakes decisions such as whether to credential surgeons and grant them the privilege to operate on patients, it is critical that they treat all surgeons fairly. However, it remains an open question whether surgical AI systems exhibit bias against surgeon sub-cohorts, and, if so, whether such bias can be mitigated. Here, we examine and mitigate the bias exhibited by a family of surgical AI systems-SAIS-deployed on videos of robotic surgeries from three geographically-diverse hospitals (USA and EU). We show that SAIS exhibits an underskilling bias, erroneously downgrading surgical performance, and an overskilling bias, erroneously upgrading surgical performance, at different rates across surgeon sub-cohorts. To mitigate such bias, we leverage a strategy -TWIX-which teaches an AI system to provide a visual explanation for its skill assessment that otherwise would have been provided by human experts. We show that whereas baseline strategies inconsistently mitigate algorithmic bias, TWIX can effectively mitigate the underskilling and overskilling bias while simultaneously improving the performance of these AI systems across hospitals. We discovered that these findings carry over to the training environment where we assess medical students' skills today. Our study is a critical prerequisite to the eventual implementation of AI-augmented global surgeon credentialing programs, ensuring that all surgeons are treated fairly.

Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays

This paper aims to identify uncommon cardiothoracic diseases and patterns on chest X-ray images. Training a machine learning model to classify rare diseases with multi-label indications is challenging without sufficient labeled training samples. Our model leverages the information from common diseases and adapts to perform on less common mentions. We propose to use multi-label few-shot learning (FSL) schemes including neighborhood component analysis loss, generating additional samples using distribution calibration and fine-tuning based on multi-label classification loss. We utilize the fact that the widely adopted nearest neighbor-based FSL schemes like ProtoNet are Voronoi diagrams in feature space. In our method, the Voronoi diagrams in the features space generated from multi-label schemes are combined into our geometric DeepVoro Multi-label ensemble. The improved performance in multi-label few-shot classification using the multi-label ensemble is demonstrated in our experiments (The code is publicly available at https://github.com/Saurabh7/Few-shot-learning-multilabel-cxray).

A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology

BACKGROUND

The field of machine learning is being employed more and more in medicine. However, studies have shown that the quality of published studies frequently lacks completeness and adherence to published reporting guidelines. This assessment has not been done in the subspecialty of anesthesiology.

METHODS

We appraised the quality of reporting of a convenience sample of 67 peer-reviewed publications sourced from the scoping review by Hashimoto et al. Each publication was appraised on the presence of reporting elements (reporting compliance) selected from 4 peer-reviewed guidelines for reporting on machine learning studies. Results are described in several cross sections, including by section of manuscript (e.g. abstract, introduction, etc.), year of publication, impact factor of journal, and impact of publication.

RESULTS

On average, reporting compliance was 64% ± 13%. There was marked heterogeneity of reporting based on section of manuscript. There was a mild trend towards increased quality of reporting with increasing impact factor of journal of publication and increasing average number of citations per year since publication, and no trend regarding recency of publication.

CONCLUSION

The quality of reporting of machine learning studies in anesthesiology is on par with other fields, but can benefit from improvement, especially in presenting methodology, results, and discussion points, including interpretation of models and pitfalls therein. Clinicians in today's learning health systems will benefit from skills in appraisal of evidence. Several reporting guidelines have been released, and updates to mainstream guidelines are under development, which we hope will usher in improvement in reporting quality.