Compilation video of differing events in surgeries on different patients

1. A computer-implemented method for surgical preparation, the method comprising:

accessing a repository of a plurality of sets of surgical video footage reflecting a plurality of surgical procedures performed on differing patients and including intraoperative surgical events, surgical outcomes, patient characteristics, surgeon characteristics, and intraoperative surgical event characteristics;

enabling a surgeon preparing for a contemplated surgical procedure to input case-specific information corresponding to the contemplated surgical procedure;

comparing the case-specific information with data associated with the plurality of sets of surgical video footage to identify a group of intraoperative events likely to be encountered during the contemplated surgical procedure;

using the case-specific information and the identified group of intraoperative events likely to be encountered to identify specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events, wherein the identified specific frames include frames from the plurality of surgical procedures performed on differing patients;

determining, using an implementation of a machine learning model, that a first set and a second set of video footage from differing patients contain frames associated with intraoperative events sharing a common characteristic, the machine learning model being trained using example video footage to determine whether two example sets of video footage share the common characteristic;

omitting an inclusion of the second set from a compilation to be presented to the surgeon and including the first set in the compilation to be presented to the surgeon; and

enabling the surgeon to view a presentation including the compilation containing frames from the differing surgical procedures performed on differing patients.

2. The method of claim 1 , further comprising enabling a display of a common surgical timeline including one or more chronological markers corresponding to one or more of the identified specific frames along the presentation.

3. The method of claim 1 , wherein enabling the surgeon to view the presentation includes sequentially displaying discrete sets of video footage of the differing surgical procedures performed on differing patients.

4. The method of claim 3 , wherein sequentially displaying discrete sets of video footage includes displaying an index of the discrete sets of video footage enabling the surgeon to select one or more of the discrete sets of video footage.

5. The method of claim 4 , wherein the index includes a timeline parsing the discrete sets into corresponding surgical phases and textual phase indicators.

6. The method of claim 5 , wherein the timeline includes an intraoperative surgical event marker corresponding to an intraoperative surgical event, and wherein the surgeon is enabled to click on the intraoperative surgical event marker to display at least one frame depicting the corresponding intraoperative surgical event.

7. The method of claim 1 , wherein the case-specific information corresponding to the contemplated surgical procedure is received from an external device.

8. The method of claim 1 , wherein comparing the case-specific information with data associated with the plurality of sets of surgical video footage includes using an artificial neural network to identify the group of intraoperative events likely to be encountered during the contemplated surgical procedure.

9. The method of claim 8 , wherein using the artificial neural network includes providing the case-specific information to the artificial neural network as an input.

10. The method of claim 1 , wherein the case-specific information includes a characteristic of a patient associated with the contemplated procedure.

11. The method of claim 10 , wherein the characteristic of the patient is received from a medical record of the patient.

12. The method of claim 11 , wherein the case-specific information includes information relating to a surgical tool.

13. The method of claim 12 , where the information relating to the surgical tool includes at least one of a tool type or a tool model.

14. The method of claim 11 , wherein the common characteristic includes a characteristic of the differing patients.

15. The method of claim 1 , wherein the common characteristic includes an intraoperative surgical event characteristic of the contemplated surgical procedure.

16. The method of claim 1 , wherein the method further comprises training an additional machine learning model to generate an index of the repository based on the intraoperative surgical events, the surgical outcomes, the patient characteristics, the surgeon characteristics, and the intraoperative surgical event characteristics; and generating the index of the repository, and wherein comparing the case-specific information with data associated with the plurality of sets includes searching the index.

17. A surgical preparation system, comprising:

at least one processor configured to:

access a repository of a plurality of sets of surgical video footage reflecting a plurality of surgical procedures performed on differing patients and including intraoperative surgical events, surgical outcomes, patient characteristics, surgeon characteristics, and intraoperative surgical event characteristics;

enable a surgeon preparing for a contemplated surgical procedure to input case-specific information corresponding to the contemplated surgical procedure;

compare the case-specific information with data associated with the plurality of sets of surgical video footage to identify a group of intraoperative events likely to be encountered during the contemplated surgical procedure;

use the case-specific information and the identified group of intraoperative events likely to be encountered to identify specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events, wherein the identified specific frames include frames from the plurality of surgical procedures performed on differing patients;

determine, using an implementation of a machine learning model, that a first set and a second set of video footage from differing patients contain frames associated with intraoperative events sharing a common characteristic, the machine learning model being trained using example video footage to determine whether two example sets of video footage share the common characteristic;

omit an inclusion of the second set from a compilation to be presented to the surgeon and including the first set in the compilation to be presented to the surgeon; and

enable the surgeon to view a presentation including the compilation and including frames from the differing surgical procedures performed on differing patients.

18. A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to execute operations enabling surgical preparation, the operations comprising:

accessing a repository of a plurality of sets of surgical video footage reflecting a plurality of surgical procedures performed on differing patients and including intraoperative surgical events, surgical outcomes, patient characteristics, surgeon characteristics, and intraoperative surgical event characteristics;

enabling a surgeon preparing for a contemplated surgical procedure to input case-specific information corresponding to the contemplated surgical procedure;

comparing the case-specific information with data associated with the plurality of sets of surgical video footage to identify a group of intraoperative events likely to be encountered during the contemplated surgical procedure;

using the case-specific information and the identified group of intraoperative events likely to be encountered to identify specific frames in specific sets of the plurality of sets of surgical video footage corresponding to the identified group of intraoperative events, wherein the identified specific frames include frames from the plurality of surgical procedures performed on differing patients;

determining, using an implementation of a machine learning model, that a first set and a second set of video footage from differing patients contain frames associated with intraoperative events sharing a common characteristic, the machine learning model being trained using example video footage to determine whether two example sets of video footage share the common characteristic;

omitting an inclusion of the second set from a compilation to be presented to the surgeon and including the first set in the compilation to be presented to the surgeon; and

enabling the surgeon to view a presentation including the compilation and including frames from the differing surgical procedures performed on differing patients.