Over the last decades, technological advances have brought great new opportunities into the hands of physicians. When these advances crystallize as new algorithms applied to available data, several ways are available to use them. Some algorithms require no human intervention at all, and can be provided as stand-alone software packages that can be run from a standard personal computer. However, many advanced methods are more complex and require highly trained personnel and substantial computation power to generate trustworthy results. These in turn can be interpreted by the physician and relied upon to make diagnostic or therapeutic decisions. Historically, applying such methods in clinical practice de facto required dedicated personnel – either within the clinical group or in a closely associated research group.

Several advanced signal and image processing techniques have been suggested, studied and often validated for the presurgical evaluation of patients with medically refractory epilepsy. The incentive for investigating these techniques stems from the difficulty of defining the epileptogenic focus, where epileptologists are sometimes grasping at straws to find information that generates a good hypothesis. Unfortunately, despite often very promising results of validation studies and successful use of such techniques in centers that have dedicated personnel, the wide dissemination is hampered by the technological barriers that exist for centers who do not have these resources.

Electrical source imaging is a prime example of this. Although there is a large body of evidence supporting the usefullness of electrical source imaging (ESI) of interictal epileptiform discharges (spikes) as a source of extra information for the presurgical evaluation, a recent survey in Europe found that less than 40% of centers are using ESI. This is, at least in part, due to the barrier generated by the need for dedicated personnel and computing power to perform ESI with state-of-the-art methods: using a patient-specific head model with segmentation into six different tissue classes and based on high-quality spikes.

This is exactly what we wish to tackle with Epilog PreOp. By offering automatic spike detection and ESI as a service model, the technological and logistic barrier is reduced to a simple up- and download of  EEG and MRI data. We take care of the full complexity of the process of state-of-the-art ESI, and provide the epileptologist with a concise report that provides all the relevant results. This entirely obliterates the need for dedicated personnel and computing power. The extra information can be integrated into the presurgical workup with minimal added effort. This makes it possible for the epileptologist to spend less time processing data, and more time interpreting results to generate the best treatment plan for the patient.