ML studies should be held to the same quality standards as any other diagnostic or prognostic study. Several frameworks exist that define standard protocol items for clinical trials as well as for reporting the results of diagnostic and prognostic studies. Clinical trial protocols should conform to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) checklist . Diagnostic accuracy studies to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) requirements and, at a minimum, should report essential items listed in the 2015 version of the STARD checklist . For prognostic studies, the Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guideline and checklist  should be followed. Although these guidelines were not designed with ML studies in mind, they do form a solid basis for providing the details of a ML study in a protocol (SPIRIT), and for reporting results of studies in which ML has been applied (STARD and TRIPOD). Because these guidelines have not been taken up widely in the ML community, efforts are underway to develop ML-specific versions of each of these frameworks. In the meanwhile, we attempt to provide guidance by offering a checklist of items for researchers designing ML studies and for readers assessing the quality of published reports. Our efforts expand upon the recently published editorial by Bluemke et al, which also addresses this topic .
Recommended items for designing and reporting ML studies
In the following section, we provide a list of important considerations when designing and reading studies that employ ML. We have summarized these considerations in a checklist (Table 1) and apply them to a research article that aimed to design a DL algorithm for automatic cardiac chamber segmentation and quantification of left ventricular ejection fraction (LVEF; ; Table 2).
Which clinical problem is being solved?
A clear description of the clinical problem and rationale for the study should be provided, taking into account existing approaches and how they fall short. This includes the specification of the disease in question and a clear description of the subjects or patients studied. It is also important to hypothesize how ML approaches may improve upon existing approaches such as conventional statistical approaches to solve the problem. Other relevant questions include the stage of the disease in question and place in the diagnostic pathway.
Choice of ML model
The choice of ML model should be clearly motivated since there is a wide variety of approaches, which may result in different results. It is also important to explicitly discuss overfitting and approaches used to mitigate this problem. Overfitting occurs when ML models are trained to predict training data too well, which results in the inability to generalize to new, unseen data. An overview of commonly used ML models and their characteristics as well as approaches that can be used to deal with overfitting is provided by Liu et al in their review article . Technical details of the algorithm including hyperparameters should be specified to foster transparency and replicability.
Sample size motivation
In contrast to the recommendations made in the STARD and CONSORT guidelines, most ML studies have not explicitly considered sample size when designing the study and are often based on convenience samples. However, sample size and a statistical analysis plan should ideally be prespecified. Although there are presently no clear guidelines on how to calculate a sample size in ML studies, the number of subjects or datasets can be prespecified according to considerations such as the minimal clinical difference of interest or the expectation that ML is able to generate equivalent results to human observers on a certain task. Furthermore, sample sizes used by other researchers to solve comparable problems might be a good indicator.
Specification of study design and training, validation, and testing datasets
Algorithm development demands data for training, validation, and testing. Investigators should specify how the data was split into each of these categories. It is of utmost importance to strictly separate the testing dataset from the other datasets to obtain a realistic estimate of model performance. This is also a requirement for regulatory approval of ML-based computer-assisted detection devices from the United States Food and Drug Administration (FDA) . Ideally, validation is performed not only on internal data (from the same department or institute) but also on an external dataset by independent researchers.
Standard of reference
A key consideration in ML studies is selection and quality of the reference standard or ground truth. Researchers should precisely specify how and by whom ground truth data were labeled, including the level of experience of each observer. It is important to take into account interobserver variability between experts and to describe how disagreements are resolved (e.g., by demanding that observers reach a consensus, or by adjudicating any differences by a separate observer). It should be noted whether existing labels were used (e.g., from radiology reports or electronic health records), or new labels were created. Finally, experts labeling the data should ideally work independently from each other because this will facilitate measurement of interobserver agreement between human experts.
Reporting of results
Analogous to conventional diagnostic studies, contingency tables with the number of true positive, true negative, false positive, and false-negative classifications should be given at the prespecified chosen classifier threshold. Other useful measures include the area under the receiver operating curve (AUC) and Bland-Altman plots . It is important to note that terminology in ML studies may be different from the terminology used in the medical literature. Sensitivity is equivalent to “recall” and “precision” denotes positive predictive value. The F1 score is a compound measure of precision and recall and its use is therefore highly recommended. Table 3 summarizes measures frequently used in ML. Confidence intervals should be reported for all of these measures. In image segmentation and analysis tasks, measures of how well the ML algorithm performs compared to the standard of reference should be given. These typically include the Dice coefficient (a measure of how well the ML generated contours overlap with the standard of reference contours), the mean contour distance (the mean distance between two segmentation contours), and the Hausdorff distance (the maximum distance between the 2 segmentation contours) .
Are the results explainable?
Because of the large number of parameters involved, interpreting the results of ML studies can be challenging, especially when working with DL algorithms. This consideration is particularly pertinent when important treatment decisions are contingent upon the results generated by the algorithm. Saliency mapping enables the identification of morphological features in the input image underlying the model’s prediction and can help to investigate the algorithm’s internal logic. Visual feedback about the model’s predictions is very important to understand whether networks learn patterns agreeing with accepted pathophysiological features or biologically unknown, potentially irrelevant features.
Can the results be applied in a clinical setting?
Machine learning studies designed to solve a specific clinical problem should explicitly consider whether the results apply to a real-world clinical setting. This includes discussion of how representative the dataset used for derivation and testing of the model is of the clinical setting in which it will be applied. Any sources of bias, in particular class imbalance and spectrum bias, should be identified and discussed. Considering these factors can enable more precise identification of patients in which the algorithm can be used clinically, or in which groups of patients and clinical scenarios additional validation is needed. Investigators should also consider if and how the algorithm can be used at the point of care, including issues like availability of the algorithm (e.g., on-premise or via cloud solutions), how fast results are available (e.g., in real-time or with a delay), and how results are visualized in order to check the model’s predictions.
Is performance reproducible and generalizable?
To date, in most reports on ML, model development, tuning, and testing have been performed on a convenience sample of locally available data. Although many of these reports have demonstrated encouraging results, it is important to investigate the reproducibility of the model and to perform an external validation, preferably on multiple datasets from other independent institutes and investigators. External validation is important to investigate the robustness of the model to e.g. differences in image acquisition and reconstruction methods between different vendors and institutes and differences in referral patterns and variability in the prevalence of the condition of interest. Conversely, we also believe it is advisable to validate external algorithms prior to local use, especially if the algorithms` results are used for automated analysis with results directly transferred into clinical reports instead of use as a second reading tool.
Is there any evidence that the model has an effect on patient outcomes?
Although one of the first proofs of concept in the development of an ML algorithm is the investigation of its diagnostic accuracy, investigators and readers should ask themselves the question whether there is any evidence of an effect on patient outcomes. This is especially important for algorithms used for treatment recommendations and detection of unrequested findings. Ideally, this should be investigated in prospective, randomized clinical trials, as is the case for conventional interventions. These considerations also help to detect and mitigate reasons for missing impact of diagnostically well performing algorithms on patient outcomes, such as suboptimal communication of results.
Is the code available?
Transparency regarding an ML model’s design and function is key to clinical acceptance. Making the computer code available to other investigators is a major step towards this goal and is increasingly becoming a condition for obtaining funding as well as acceptance of studies in high-quality, peer-reviewed journals. The GitHub platform facilitates free and rapid dissemination of software code with basic quality checks. Investigators should state whether the source code of their algorithm will be made available and under which conditions. If not, specific reasons should be given. Making the software code available enables other researchers to independently investigate whether reported results can be reproduced and to improve model performance. Furthermore, it enables the evaluation of a model’s performance over a prolonged period of time.