Checklist of key elements that should be described in machine learning papers, structured by manuscript section
| Methods | Page | |
| Data | Minimise risk of overfitting by the use of multiple, heterogeneous and independent datasets. | |
| Provide a complete description of the data acquisition process. | ||
| Describe the basic technical information of the imagery and the use of any preprocessing methods. | ||
| Define a reliable gold standard for all data used to train, validate and test the model. | ||
| Algorithms that localise lesions on images and videos, reliable gold standard input for the model should incorporate annotations by multiple experts. | ||
| Provide detailed information on ethics approval concerning the use of patient data. | ||
| Algorithm architecture | Provide a basic description of the algorithm architecture and clear-cut motivation for the most relevant technical choices. | |
| Describe extensive technical details in separate technical publications, or in supplementary materials. | ||
| Experimental set-up | Describe the experimental set-up of the algorithm and choose the appropriate performance metrics. | |
| Primary outcome parameters should be based on the envisioned clinical application of the model. | ||
| Do not optimise hyperparameters on test set. | ||
| Ensure that training, validation and test sets are always split on a patient-basis. | ||
| Report a complete overview of all evaluated models to prevent ‘cherry-picking’ of the best performing algorithms. | ||
| Results | ||
| Results should be presented with caution and in a structured approach. | ||
| Discussion | ||
| Include a section where data selection bias, overfitting and generalisability are explicitly discussed. | ||
| Describe the necessary steps towards clinical implementation. |