Homogeneity in Surgical Series: Image Reporting to Improve Evidence.
Pietro RegazzoniSimon LambertJesse B JupiterNorbert SüdkampWen-Chih LiuAlberto A Fernández Dell'OcaPublished in: Journal of clinical medicine (2023)
Good clinical practice guidelines are based on randomized controlled trials or clinical series; however, technical performance bias among surgical trials is under-assessed. The heterogeneity of technical performance within different treatment groups diminishes the level of evidence. Surgeon variability with different levels of experience-technical performance levels even after certification-influences surgical outcomes, especially in complex procedures. Technical performance quality correlates with the outcomes and costs and should be measured by image or video-photographic documentation of the surgeon's view field during the procedures. Such consecutive, completely documented, unedited observational data-in the form of intra-operative images and a complete set of eventual radiological images-improve the surgical series' homogeneity. Thereby, they might reflect reality and contribute towards making necessary changes for evidence-based surgery.
Keyphrases
- deep learning
- randomized controlled trial
- convolutional neural network
- electronic health record
- minimally invasive
- optical coherence tomography
- type diabetes
- artificial intelligence
- coronary artery bypass
- systematic review
- machine learning
- big data
- clinical trial
- adipose tissue
- skeletal muscle
- adverse drug
- atrial fibrillation
- insulin resistance
- data analysis
- advance care planning