Computer-implemented method for determining planning data for a surgical procedure on a subject - Patent Application 20070122997

Abstract

1. A computer-implemented method for determining planning data for a surgical process of a subject in a urological procedure, the method comprising: providing, by a processor, a predictive model trained to predict planning data based on at least one of past patient data, past task data, past planning data, current patient data, and current task data; obtaining, by the processor, at least one of the current patient data and the current task data; inputting the at least one of the current patient data and the current task data into the predictive model to determine planning data; and providing, by the processor, a user recommendation regarding planning of the urological procedure plan, such as a PCNL procedure, based on the determined planning data.

Description

[Technical Field]

[0001] The present invention relates to a computer-implemented method for determining planning data for a surgical procedure on a subject, an apparatus for determining planning data for a surgical procedure on a subject, and a computer program product. [Background technology]

[0002] Medical procedures such as surgery, e.g., percutaneous nephrolithotomy (PCNL), are difficult to plan due to multiple influencing factors. However, the quality and efficiency of such procedures depend on reliable planning data. Summary of the Invention [Problem to be solved by the invention]

[0003] It has now become apparent that there is a further need to provide a method for determining planning data.

[0004] These and other objects that will become apparent on reading the following description are solved by the subject matter of the independent claims. The invention provides a method, an apparatus and a computer program. The dependent claims refer to preferred embodiments of the invention. [Means for solving the problem]

[0005] In view of the above, it is an object of the present invention to provide a method that allows to improve the determination of planning data for surgical procedures on a subject, in particular a human being.

[0006] The inventors of the present application have recognized that some medical procedures, such as urological procedures (e.g., percutaneous nephrolithotomy (PCNL): a minimally invasive procedure for breaking up kidney stones and removing them from the kidney through a small puncture incision in the skin), have high complication rates. Thus, planning a PCNL procedure can be challenging in terms of duration, required equipment, level of expertise, and personnel, including personnel from other departments, including personnel who must be on-call.

[0007] These and other objects that will become apparent on reading the following description are solved by the subject matter of the independent claims. The invention provides a method, an apparatus and a computer program. The dependent claims refer to preferred embodiments of the invention.

[0008] Planning a surgical procedure is challenging given the clinical complexity (e.g., various different patient factors that must be considered) and the complexity of clinical workflow. For example, in PCNL, not only the stone size, the associated risk of stone fragments blocking the ureter, and the stone composition, but also the patient's obesity and the proximity of the calyx selected for initial puncture to at-risk organs and arteries are determining factors in the procedure duration and the choice of cutting device. This makes every case unique with respect to the location of the calyx and at least one stone. In some cases, the entire procedure must be rescheduled due to insufficient planning data or some new factor that was not considered.

[0009] In one aspect of the present disclosure, there is provided a computer-implemented method for determining planning data for a surgical process on a subject in a urological procedure, the method comprising: providing (S100), a processor providing a predictive model trained to predict planning data based on at least one of past patient data, past task data, past planning data, current patient data, and current task data; The processor acquires (S200) at least one of current patient data and current task data; inputting the at least one of current patient data and current task data into the predictive model to determine planning data (S300); The processor provides a user recommendation regarding planning of a urological procedure plan, such as a PCNL procedure, based on the determined planning data (S400); It has.

[0010] The term "subject" is to be understood broadly in the present case and can include any human being and any animal.

[0011] The term "planning data" as used herein should be understood broadly and can relate to any data necessary or useful for planning a medical procedure, such as, for example, a urological procedure. The planning data can include at least one of staffing, equipment availability, room availability, room preparation, patient data, and planned and / or pre-access plans for the PCNL needle to the subject's area.

[0012] The term "surgical procedure" as used herein should be understood broadly and relates to any surgical procedure performed on a human or animal, which may be, for example, a urological procedure such as a PCNL procedure.

[0013] The term "predictive model" as used herein should be understood broadly and relates to any predictive model based on machine learning or artificial intelligence algorithms configured to be trained to predict planning data based on past patient data, past task data, past planning data, current patient data, and current task data, where the predictive model is trained using training data to predict planning data.

[0014] The machine learning model according to the present invention may preferably comprise at least one of a decision tree, a naive Bayes classifier, a nearest neighbor classifier, a neural network, a convolutional neural network (CNN), a generative adversarial network (GAN), a multi-conditional GAN's, a support vector machine, a linear regression, a logistic regression, a random forest, and / or a gradient boosting algorithm.

[0015] The term "historical patient data" as used herein should be understood broadly and refers to any patient data available from previous surgical procedures. It should be understood that this historical surgical procedure refers to other patients' surgical procedures and associated patient data used to compare and plan the current patient's surgical procedure. Thus, historical patient data can be received from actual surgical procedures previously performed. Much patient-specific data can be extracted from imaging data, such as computed tomography (CT), ultrasound (US), and US / MRI fusion (magnetic resonance imaging) images, as well as patient metadata (e.g., age, body circumference, obesity, body mass index (BMI), and others), provided by connected enterprise imaging systems, radiology information systems, hospital information systems, electronic health record systems, electronic medical record systems, and any combination thereof. In some embodiments, the information may be provided by the same hospital. In some embodiments, the information may be provided by different hospitals. A user can request the necessary historical patient data from at least one system, such as an electronic health record (EHR) system, via an appropriate user interface.

[0016] The term "past task data" as used herein should be understood broadly and relates to any task data available from a past surgical process. This means that the past task data is received from an actual surgical process that was previously performed. The past task data can include, for example, data regarding clinical procedures / operations performed on a patient, such as the object to be treated, clinical procedure codes, etc.

[0017] The term "past planning data" as used herein should be understood broadly and relates to any planning data available from a past surgical process. This past planning data can include initial planning data and realized planning data. This means that the past planning data is received from an actual surgical process that was performed in the past. It should be noted that the past planning data is derived, inter alia, from past task data. Thus, past task data can be at least a part of the past planning data.

[0018] The term "current patient data" as used herein should be understood broadly and relates to any patient data available for a future surgical process on a subject to be treated. This current patient data may include age, BMI, etc. This data may include past data of the same patient. The difference between "past patient data" and "current patient data" is that past patient data may include data from other patients, whereas current patient data may also include past information from the current patient.

[0019] The term "current task data" as used herein should be understood broadly and relates to any task data available for a future surgical process on a subject to be treated. This current task data may include objects to be treated, treatment methods, and object locations on the subject. As used herein, the term "current" refers to the present or future, as compared to the past, which refers to a long time ago.

[0020] The term "providing" as used herein should be understood broadly and relates to any method of presenting data. "Providing" can include presenting planning data and / or user recommendations on a screen, on a dashboard, in a graphical user interface (GUI), in a data file, in a table, etc. In some embodiments, the user can add additional entries, such as the patient's daily weight, for example, via the GUI.

[0021] In other words, the present invention relates to the use of a trained model to predict planning data for a current surgical procedure, where the model is trained using data on past surgical procedures of other patients as well as procedures on the current patient. In some embodiments, the trained model can search for similar patients based on similarity of parameters, such as comparing CT images of the patients. The combination of these data points, when the model interpolates between them, can optimally predict the characteristics of the currently planned surgical procedure. For example, a prediction may be provided that means "urologists faced with a similar task would have used the following equipment, staff, and taken 125 minutes to complete the surgical procedure." This prediction may be accompanied by diagrams, images, charts, tables, etc. This has advantages in terms of efficiency, quality, reduced complexity, and reduced errors.

[0022] Those skilled in the art will appreciate that the method is not performed during a surgical process and does not interact with the subject being treated.

[0023] Percutaneous nephrolithotripsy (PCNL) is a minimally invasive procedure for breaking up kidney stones and removing them from the kidney through a small puncture incision (up to approximately 1 cm) through the skin. An alternative to shock wave lithotripsy or ureteroscopy, PCNL is suitable for larger stones or stones with complex shapes (staghorn stones) and is usually performed under general or spinal anesthesia in an operating room by either a urologist or a combined urologist-radiologist team.

[0024] PCNL is usually planned using image data available from a diagnostic contrast-enhanced CT scan, and is performed with the patient in the prone position. Through a skin incision, under imaging guidance by either fluoroscopy or ultrasound, a hollow nephrolithotomy needle is advanced to the calyx deemed suitable for reaching the stone on the CT image, and a guidewire is inserted. Next, through a nephroscope, several dilating sheaths are inserted over the wire until an opening is large enough to remove the stone. Larger stones must first be cut using laser or cutter technology.

[0025] The placement of the guidewire to access stones is considered a more delicate part of the procedure and is more often performed by interventional radiologists than by urologists, sometimes even in a separate pre-procedure, because imprecise needle placement and insertion places the access path near delicate structures such as the vasculature, pleura, spleen, colon, and liver that can be accidentally punctured while establishing access to the renal calyx.

[0026] While PCNL is typically well tolerated, complications are relatively common and include ureteral stones (stone fragments sliding into the ureter), complete stone removal, perforation of the urinary collecting system, and vascular injury, as well as colonic perforation or pleural injury during the procedure. Staghorn kidney stones are challenging cases, requiring careful preoperative evaluation and close follow-up to avoid stone recurrence.

[0027] Because of the relatively high incidence of complications, planning a PCNL procedure can be challenging in terms of duration, required equipment, level of expertise, and personnel, including on-call personnel. Factors that need to be considered are patient- or stone-related, including obesity, proximity of the calyx selected for initial puncture to organs and arteries at risk, as well as stone size, the associated risk of stone fragments blocking the ureter, stone composition, which are determining factors during the procedure, and choice of cutting device. This makes every case unique with respect to the anatomy of the calyx and stone location.

[0028] For example, obese patients present several technical challenges, including anesthesia, patient positioning, imaging for access, longer distances from the skin to the collecting system, and removal of the nephrostomy tube.

[0029] The present invention proposes using AI-based algorithms, e.g., machine learning algorithms, to provide the best prediction of planned procedure characteristics of interest, such as expected duration, potential complications, staff needs, and equipment needs. The present invention can provide a user-facing system that allows input of all details that cannot be captured automatically. The present invention can present the prediction to the physician (e.g., urologist) via a user interface.

[0030] The present invention can provide a database that records information for each case with stone details such as number, size, location, composition, procedure details such as planned and realized access route, route risk (distance to organs and structures at risk), duration of use, complications, equipment used, current staff (including experience level), procedural success (stone free), and patient details such as age, comorbidities, BMI.

[0031] According to one embodiment, the user recommendations may include recommendations regarding at least one of workflow adjustments, risk stratification, possible side effects, and treatment recommendations.

[0032] According to one embodiment, the procedural recommendations are from at least one of the planned and realized access route, distance to organs and structures at risk, recommended procedural duration, recommended equipment, and staff availability.

[0033] According to one embodiment, the method may further comprise providing, by the processor, a database comprising past patient data, past task data, and past plan data. This is advantageous as it may increase the reliability of the predictive model. As used herein, the term database, which should be understood broadly, relates to any digital database configured to store data in an organized manner.

[0034] According to one embodiment, the method may further comprise storing the current patient data, the current task data, the determined plan data, and the realized plan data in a database. This is advantageous as it leads to a higher predictive quality of the trained model. The predictive model may be trained continuously. The current patient data, the current task data, the determined plan data, and the realized task data may be received automatically or manually by the database using a data interface. If received manually, this interface may be, for example, an HMI (i.e., user interface) configured to receive input from staff performing the surgical process.

[0035] According to one embodiment, a method is provided that further comprises receiving past patient data, past task data, and / or past plan data via a user interface. This is advantageous because certain data, such as observations made by staff, cannot be received automatically. The user interface may be a tablet, PC, HMI, or the like.

[0036] According to one embodiment, a method is provided that comprises selecting a set of past patient data, past task data, and past planning data by utilizing a predictive model and acquired current patient data and current task data, and providing the selected set. This is advantageous because the set details who will perform the surgical process and how similar cases have been performed in the past. Thus, the selected data from the database can serve as a supplementary support to the determined planning data.

[0037] According to one embodiment, the patient data may include at least one of age, comorbidities, and BMI, and / or the past planning data may include at least determined planning data and realized planning data. The term realized planning data relates to realized planning data for performing a surgical process, such as an realized access path to a kidney stone. The realized access path to a kidney stone may deviate from a previously determined access path. This may also be advantageous for a physician, as it may help the physician classify the determined planning data in terms of reliability.

[0038] According to one embodiment, the patient data may include CT images of the subject and / or at least some of the patient data may be extracted from the CT images, which is advantageous as the CT images and corresponding information provide more detailed information.

[0039] According to one embodiment, the task data can include at least information regarding an object located within the subject to be treated and / or information regarding a standard access route to the object within the subject. The standard access route may include, for example, a particular calyx where a kidney stone is located. The information can be presented in a modified CT image that can show the access route and the location of the kidney stone. This can reduce the complexity of future surgeries.

[0040] According to one embodiment, the planning data may include at least one of the following: equipment required for the surgical process, duration of the surgical process, staff required for the surgical process, risk of adverse events, and access routes to objects located within the subject to be treated, which is advantageous in terms of the quality of the plan.

[0041] According to one embodiment, the planning data may include contact information for the attending physician, which may be useful for receiving further information if questions still remain. The contact information may include an address, a phone number, or an email address. The contact information may be searched for cases similar to the current case from a database, where the case is described or defined by the planning data, patient data, and task data.

[0042] According to one embodiment, the method may comprise determining an uncertainty measure for the determined planning data. The term "uncertainty measure" as used herein should be understood broadly and relates to the uncertainty of the determined planning data. The uncertainty measure may be determined by using a predictive model. In other words, the uncertainty measure is a further result of the predictive model, which indicates the quality of the predicted result. The uncertainty measure may be presented in the same manner as the planning data described above. The uncertainty measure may be a scale such as 0 to 1 or 0 to 100%. The uncertainty measure may also be used to determine rare cases, which is useful information for staff assigned to a particular case.

[0043] According to one embodiment, a case report of the surgical process is generated based on the determined uncertainty measure and provided for further processing. For example, if the uncertainty measure is below a threshold value (e.g., 80%), the current surgery represents a rare case and a case report is generated. This case report can be used for further analysis of planning data, patient data, and task data. This helps to better understand and recognize difficult cases. The case report can include any data available for a particular surgery. Since predictive models can be trained to also provide uncertainty measures for these predictions, they are not only presented but also used to identify rare cases, i.e., cases whose characteristics are predicted only with a lot of uncertainty. This can be presented to the user along with a recommendation to publish the case report. These case reports can also be used to train young specialists.

[0044] According to one embodiment, the trained prediction model is trained continuously, i.e., current patient data, determined planning data, and current task data are used to train the prediction model, which is advantageous in terms of the quality and reliability of the predictions.

[0045] A further aspect relates to an apparatus for determining planning data for a surgical procedure on a subject, comprising means for performing the steps of the method described above. The means may comprise a processor, a computer unit, a workstation with corresponding interfaces for providing the predictive model, acquiring patient data, task data and determining the planning data. The means may comprise a screen, dashboard, tablet etc. for providing and presenting the determined planning data.

[0046] A final aspect relates to a computer program having instructions which, when executed by a computer, cause the computer to carry out the above-mentioned method, and / or a computer-readable medium having instructions which, when executed by a computer, cause the computer to carry out the above-mentioned method.

[0047] A computer program may be stored on a computer unit, which may be part of the embodiments. This computer unit may be configured to perform or trigger the performance of the steps of the above-described method. Furthermore, the computer unit may be configured to operate components of the above-described apparatus. The computing unit may be configured to automatically operate and / or execute user instructions. The computer program may be loaded into the working memory of a data processor. The data processor may thus be equipped to perform a method according to one of the above-described embodiments. This exemplary embodiment of the present invention includes both a computer program that uses the present invention from the beginning and a computer program that, through an update, transforms an existing program into a program that uses the present invention. Furthermore, the computer program may provide all steps necessary to fulfill the procedures of the above-described exemplary embodiment of the method. According to a further exemplary embodiment of the present invention, a computer-readable medium, such as a CD-ROM or a USB stick, is presented, which has a computer program stored thereon, the computer program being described by the previous paragraph. The computer program may be stored and / or distributed on a suitable medium, for example an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, for example via the Internet or other wired or wireless communication systems. However, the computer program may also be presented over a network such as the World Wide Web and can be downloaded from such a network into the working memory of a data processor. According to a further exemplary embodiment of the present invention, a medium for making a computer program available for downloading is provided, the computer program being configured to perform a method according to one of the above-mentioned embodiments of the present invention.

[0048] It should be understood that user recommendations regarding planning a urological procedure, such as a PCNL procedure, may be displayed directly to a user of an imaging device (e.g., a urology department, an ultrasound device) or transmitted to a DICOM station. In some embodiments, the recommendations are implemented in an EHR system. In some embodiments, the displayed recommendations also include a recommendation regarding the need for consultation with an additional specialist, such as an interventional radiologist. It should be noted that the above-described embodiments may be combined with each other regardless of their associated aspects. Thus, the method may be combined with structural features of devices and / or systems of other aspects, and similarly, the devices and systems may be combined with features of each other and with features described above with respect to the method.

[0049] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter. [Brief explanation of the drawings]

[0050] Exemplary embodiments of the invention are illustrated in the following drawings. [Figure 1] FIG. 1 is a schematic diagram of a method for determining planning data for a surgical process of a subject in a urological procedure. [Figure 2] FIG. 2 shows a schematic diagram of an apparatus for determining planning data for a surgical process of a subject in a urological procedure. [Figure 3] FIG. 3 shows an exemplary image selected by a method for determining planning data for a surgical process of a subject in a urological procedure. DETAILED DESCRIPTION OF THE INVENTION

[0051] FIG. 1 is a schematic diagram of a method for determining planning data for a surgical process.

[0052] Step 100 includes providing, by a processor, a predictive model trained to predict planning data based on past patient data, past task data, past planning data, current patient data, and current task data. In this example, the predictive model may be a machine learning model. The surgical process may be percutaneous nephrolithotomy (PCNL), which is used to remove stones from the kidney via skin puncture. In this example, the subject may be a human.

[0053] The predictive model is a model trained using past patient data, past task data, and past planning data, which may include at least determined planning data and realized planning data.

[0054] The planning data may include one of the following: equipment required for the surgical process, duration of the surgical process, staff required for the surgical process, risk of adverse events, and access routes to objects located within the subject to be treated.

[0055] The task data (past and present) may include at least information regarding objects located within the subject being treated and / or information regarding standard access routes to objects within the subject. In this example, the task data may include the location, size, and / or shape of one or more stones within the kidney. The standard access route may include one or more renal calyces suitable for reaching the stones. The standard access route is depicted on the CT image.

[0056] The patient data comprises at least one of age, comorbidities, and BMI. The patient data may comprise CT images of the subject and / or at least part of the patient data is extracted from the CT images. The planning data (past and current) may comprise contact details of the attending physician. The processor may be part of a computer unit, workstation, or virtual machine. The processor may be a single entity or distributed across multiple entities.

[0057] Step 200 includes obtaining, by a processor, current patient data and current task data. The current patient data and current task data may be obtained automatically by an interface between the processor and a database system (e.g., a hospital database system). The current patient data and current task data may be obtained manually from user input via an HMI, where staff (e.g., a physician) enters the current patient data and current task data.

[0058] In this example, the current patient data may include the patient having a BMI of 26.6 and being 36 years old. The current task data may include the presence of a single stone having a generally round shape that must be removed relative to a reference point. The current patient data may include corresponding diagnostic CT images.

[0059] Step 300 includes inputting at least one of current patient data and current task data into a predictive model to determine planning data, the predictive model receiving the current patient data and the current task data as inputs and providing planning data as outputs.

[0060] The determined planning data includes interpolations between data points / data sets that are used to train the predictive model. The determined planning data can then include, for example, that certain tools are required, an access route through a certain calyx is proposed, a urologist is required, the procedure will last 85 minutes, and no complications are expected.

[0061] Step 400 includes providing, by the processor, user recommendations regarding planning of a urological procedure plan, such as a PCNL procedure, based on the determined planning data. The user recommendations may be represented as diagrams, reports, tables, or adaptive CT images. The user recommendations may be presented on a screen or dashboard.

[0062] Additionally, the user recommendations may include recommendations regarding at least one of workflow adjustments, risk stratification, possible side effects, and treatment recommendations.

[0063] Additionally, procedural recommendations are derived from at least one of the following: planned and realized access route, distance to organ, structures at risk, recommended procedure duration, recommended equipment, and staff availability.

[0064] The method may further include receiving, via a user interface, past patient data, past task data, and / or past planning data. The method may further include selecting a set of past patient data, past task data, and past planning data by utilizing a predictive model and the obtained current patient data and current task data, and providing the selected set.

[0065] The method can further comprise determining an uncertainty measure for the determined planning data. Based on the determined uncertainty measure, a case report of the surgical process is generated and provided for further processing. The predictive model can be continuously trained using any further received datasets of the currently performed surgery.

[0066] FIG. 2 shows a schematic diagram of an apparatus for determining planning data for a surgical process of a subject in a urological procedure.

[0067] The device 100 comprises a processor. This processor may be part of a computer unit. This computer unit may be configured to perform or trigger the performance of the steps of the above-mentioned method. In this example, the processor is part of a workstation located in a hospital. Alternatively, the processor may be located in a cloud application and accessed by an interface and a corresponding communication unit. The device may further comprise presentation means, for example a screen or a dashboard. The device may further comprise one or more interfaces for data exchange.

[0068] FIG. 3 shows an exemplary image selected by a method for determining planning data for a surgical process of a subject in a urological procedure.

[0069] 3 has four diagnostic CT images 100, 101, 102, and 103. Image 100 shows a diagnostic CT image of a subject being treated in two views 106, 107, each with access paths 104 and 105. Diagnostic CT images 101, 102, and 103 refer to diagnostic CT images from previous cases stored in a database.

[0070] Diagnostic CT images 101, 102, and 103 each also show two views and corresponding access paths. Diagnostic CT images 101, 102, and 103 are selected from a number of cases stored in a database by the method described above.

[0071] The selected diagnostic CT images 101, 102, and 103 show the highest similarity with the current task data and the current patient data. In particular, diagnostic CT image 103 shows the best match among the three images 101, 102, and 103. This set of CT images is presented to a user, e.g., a doctor, who has to perform the surgery, for example, during the planning stage. [Explanation of symbols]

[0072] S100: Providing a trained predictive model S200: Acquiring current patient data and current task data S300: Step for determining planning data S400: Providing user recommendations 10: Equipment 100, 101, 102, 103: Diagnostic CT images 104, 105: Access route 106, 107: Different views

Claims

1. 1. A computer-implemented method for determining planning data for a surgical process on a subject in a urological procedure, the method comprising: providing a predictive model trained by a processor to predict planning data based on at least one of past patient data, past task data, past planning data, current patient data, and current task data; the processor obtaining at least one of current patient data and current task data; inputting the at least one of current patient data and current task data into the predictive model to determine planning data; the processor providing a user recommendation regarding planning of a urological procedure plan based on the determined planning data.

10. A computer-implemented method comprising:

2. The method of claim 1 , wherein the user recommendations comprise recommendations regarding at least one of workflow adjustments, risk stratification, possible side effects, and treatment recommendations.

3. 3. The method of claim 2, wherein the procedural recommendations are from at least one of planned and realized access routes, distance to organs, structures at risk, recommended procedure duration, recommended equipment, and staff availability.

4. The method of claim 1 , further comprising receiving, via a user interface, past patient data, past task data, and / or past plan data.

5. 3. The method of claim 2, further comprising selecting a set of past patient data, past task data, and past planning data by utilizing the predictive model and the acquired current patient data and current task data, and providing the selected set.

6. the patient data comprises at least one of age, comorbidities, BMI; and / or The method according to claim 1 , wherein the past planning data comprises at least determined planning data and realized planning data.

7. the patient data comprises CT images of the subject; and / or The method of claim 1 , wherein at least some of the patient data is extracted from the CT images.

8. 8. The method of claim 1, wherein the task data comprises at least information about an object located within the subject to be treated and / or information about a standard access route to the object within the subject.

9. 9. The method of claim 1, wherein the planning data comprises at least one of equipment required for the surgical process, duration of the surgical process, staff required for the surgical process, risk of adverse events, and access routes to objects located within the subject to be treated.

10. The method of claim 1 , wherein the planning data further comprises contact details of an attending physician, the planning data comprising contact details of the attending physician.

11. 11. The method of claim 1, further comprising determining an uncertainty measure for the determined planning data.

12. The method of claim 11 , wherein, based on the determined measure of uncertainty, case reports of the surgical process are selected and provided for further processing.

13. The method of claim 1 , wherein the trained predictive model is trained continuously.

14. 10. An apparatus for determining planning data for a surgical procedure on a subject, comprising means for carrying out the steps of the method of claim 1.

15. 14. A computer program having instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 13, and / or a computer-readable medium having instructions which, when executed by a computer, cause the computer to carry out the method of any one of claims 1 to 13.

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