Machine learning in neurosurgery: a global survey

Staartjes, Victor E.; Stumpo, Vittorio; Kernbach, Julius M; Klukowska, Anita M.; Gadjradj, Pravesh S.; Schröder, Marc; Veeravagu, Anand; Stienen, Martin N.; Niftrik, Christiaan Hendrik Bas van; Serra, Carlo; Regli, Luca

doi:10.1007/s00701-020-04532-1

Cited by 62 publications

(29 citation statements)

References 43 publications

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“…Despite the easiness, advantages, and future potential of DL, the majority of medical staff cannot treat DL software. [ 17 ] However, as simple DL software like Prediction One is being developed, there is a need for an active interest in using it to benefit medical staff and patients. [ 5 ] Our study is just one example and suggested the utility of DL software.…”

Section: Discussionmentioning

confidence: 99%

Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire

Katsuki

Narita

Matsumori³

et al. 2020

Surgical Neurology International

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Background: Primary headaches are widespread and costly public health problems. However, there are insufficient medical resources for their treatment in Japan due to two reasons. First, the numbers of headache specialists and clinics remain insufficient. Second, neurologists and neurosurgeons mainly treat headaches in Japan. However, they mainly work as general stroke neurologists, so they cannot focus on primary headache treatment. To solve these problems, we preliminarily developed a deep learning (DL)-based automated diagnosis model from patients’ Japanese unstructured sentences in the medical questionnaire using a DL framework. We hypothesized that the model would reduce the time and burden on both doctors and patients and improve their quality of life. Methods: We retrospectively investigated our primary headache database and developed a diagnosis model using the DL framework (Prediction One, Sony Network Communications Inc., Japan). We used age, sex, date, and embedding layer made by the medical questionnaire’s natural language processing (NLP). Results: Eight hundred and forty-eight primary headache patients (495 women and 353 men) are included. The median (interquartile range) age was 59 (40–74). Migraine accounted for 46%, tension-type headache for 47%, trigeminal autonomic cephalalgias for 5%, and other primary headache disorders for 2%. The accuracy, mean precision, mean recall, and mean F value of the developed diagnosis model were 0.7759, 0.8537, 0.6086, and 0.6353, which were satisfactory. Conclusion: The DL-based diagnosis model for primary headaches using the raw medical questionnaire’s Japanese NLP would be useful in performing efficient medical practice after ruling out the secondary headaches.

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Section: Discussionmentioning

confidence: 99%

Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire

Katsuki

Narita

Matsumori³

et al. 2020

Surgical Neurology International

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“…In a recent Swiss study exploring the attitudes of neurosurgeons toward machine learning, Staartjes et al 31 found that of the 362 participants surveyed, 29% were already implementing machine learning into their practice and a further 31% for research purposes. The most important reasons for applying machine learning to clinical practice were improved preoperative surgical decision making, objectivity in diagnosis, and improved anticipation of complications.…”

Section: Discussionmentioning

confidence: 99%

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey

Horsfall

Palmisciano²,

Khan

et al. 2021

World Neurosurgery

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Background Artificial intelligence (AI) has the potential to disrupt how we diagnose and treat patients. Previous work by our group has demonstrated that the majority of patients and their relatives feel comfortable with the application of AI to augment surgical care. The aim of this study was to similarly evaluate the attitudes of surgeons and the wider surgical team toward the role of AI in neurosurgery. Methods In a 2-stage cross sectional survey, an initial open-question qualitative survey was created to determine the perspective of the surgical team on AI in neurosurgery including surgeons, anesthetists, nurses, and operating room practitioners. Thematic analysis was performed to develop a second-stage quantitative survey that was distributed via social media. We assessed the extent to which they agreed and were comfortable with real-world AI implementation using a 5-point Likert scale. Results In the first-stage survey, 33 participants responded. Six main themes were identified: imaging interpretation and preoperative diagnosis, coordination of the surgical team, operative planning, real-time alert of hazards and complications, autonomous surgery, and postoperative management and follow-up. In the second stage, 100 participants responded. Responders somewhat agreed or strongly agreed about AI being used for imaging interpretation (62%), operative planning (82%), coordination of the surgical team (70%), real-time alert of hazards and complications (85%), and autonomous surgery (66%). The role of AI within postoperative management and follow-up was less agreeable (49%). Conclusions This survey highlights that the majority of surgeons and the wider surgical team both agree and are comfortable with the application of AI within neurosurgery.

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“…Despite the easiness, advantages, and future potential of DL, the majority of medical staff cannot treat DL frameworks. [ 45 ] As simple DL frameworks like Prediction One are being developed, there is a need for an active interest in using them to benefit medical staff and patients. Our study is just one example but suggested the utility of the DL framework.…”

Section: Discussionmentioning

confidence: 99%

Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage

Katsuki

Kakizawa

Nishikawa

et al. 2021

Surgical Neurology International

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Background: Reliable prediction models of intracerebral hemorrhage (ICH) outcomes are needed for decision-making of the treatment. Statistically making such prediction models needs a large number of samples and time-consuming statistical analysis. Deep learning (DL), one of the artificial intelligence, is attractive, but there were no reports on DL-based functional outcome prediction models for ICH outcomes after surgery. We herein made a functional outcome prediction model using DLframework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), and compared it to original ICH score, ICH Grading Scale, and FUNC score. Methods: We used 140 consecutive hypertensive ICH patients’ data in our hospital between 2012 and 2019. All patients were surgically treated. Modified Rankin Scale 0–3 at 6 months was defined as a favorable outcome. We randomly divided them into 100 patients training dataset and 40 patients validation dataset. Prediction One made the prediction model using the training dataset with 5-fold cross-validation. We calculated area under the curves (AUCs) regarding the outcome using the DL-based model, ICH score, ICH Grading Scale, and FUNC score. The AUCs were compared. Results: The model made by Prediction One using 64 variables had AUC of 0.997 in the training dataset and that of 0.884 in the validation dataset. These AUCs were superior to those derived from ICH score, ICH Grading Scale, and FUNC score. Conclusion: We easily and quickly made prediction models using Prediction One, even with a small single-center dataset. The accuracy of the DL-based model was superior to those of previous statistically calculated models.

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Machine learning in neurosurgery: a global survey

Cited by 62 publications

References 43 publications

Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire

Preliminary development of a deep learning-based automated primary headache diagnosis model using Japanese natural language processing of medical questionnaire

Attitudes of the Surgical Team Toward Artificial Intelligence in Neurosurgery: International 2-Stage Cross-Sectional Survey

Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage

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