Accuracy of thoracic nerves recognition for surgical support system using artificial intelligence.
Artificial intelligence
Lung cancer
Nerve
Recognition
Thoracoscopy
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
07 Aug 2024
07 Aug 2024
Historique:
received:
27
03
2024
accepted:
05
08
2024
medline:
8
8
2024
pubmed:
8
8
2024
entrez:
7
8
2024
Statut:
epublish
Résumé
We developed a surgical support system that visualises important microanatomies using artificial intelligence (AI). This study evaluated its accuracy in recognising the thoracic nerves during lung cancer surgery. Recognition models were created with deep learning using images precisely annotated for nerves. Computational evaluation was performed using the Dice index and the Jaccard index. Four general thoracic surgeons evaluated the accuracy of nerve recognition. Further, the differences in time lag, image quality and smoothness of movement between the AI system and surgical monitor were assessed. Ratings were made using a five-point scale. The computational evaluation was relatively favourable, with a Dice index of 0.56 and a Jaccard index of 0.39. The AI system was used for 10 thoracoscopic surgeries for lung cancer. The accuracy of thoracic nerve recognition was satisfactory, with a recall score of 4.5 ± 0.4 and a precision score of 4.0 ± 0.9. Though smoothness of motion (3.2 ± 0.4) differed slightly, nearly no difference in time lag (4.9 ± 0.3) and image quality (4.6 ± 0.5) between the AI system and the surgical monitor were observed. In conclusion, the AI surgical support system has a satisfactory accuracy in recognising the thoracic nerves.
Identifiants
pubmed: 39112794
doi: 10.1038/s41598-024-69405-4
pii: 10.1038/s41598-024-69405-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
18329Informations de copyright
© 2024. The Author(s).
Références
Ichinose, J., Kohno, T., Fujimori, S. & Mun, M. Locoregional control of thoracoscopic lobectomy with selective lymphadenectomy for lung cancer. Ann. Thorac Surg. 90, 235–239 (2010).
doi: 10.1016/j.athoracsur.2010.03.049
pubmed: 20609783
Ichinose, J. et al. Initial perioperative outcomes of robot-assisted thoracoscopic lobectomy using a confronting setting. Surg. Today 53, 1073–1080 (2023).
doi: 10.1007/s00595-023-02665-1
pubmed: 36828911
Suliburk, J. W. et al. Analysis of human performance deficiencies associated with surgical adverse events. JAMA Netw. Open 2, e198067 (2019).
doi: 10.1001/jamanetworkopen.2019.8067
pubmed: 31365107
pmcid: 6669897
Kumazu, Y. et al. Automated segmentation by deep learning of loose connective tissue to define safe dissection planes in robot-assisted gastrectomy. Sci. Rep. 11, 21198 (2021).
doi: 10.1038/s41598-021-00557-3
pubmed: 34707141
pmcid: 8551298
Sartorius-cell instance segmentation: detect single neuronal cells in microscopy images. https://www.kaggle.com/competitions/sartorius-cell-instance-segmentation/overview . (Accessed 14 Feb 2024).
Shinohara, H. Surgery utilizing artificial intelligence technology: Why we should not rule it out. Surg. Today 53, 1219–1224 (2023).
doi: 10.1007/s00595-022-02601-9
pubmed: 36192612
Madani, A. et al. Artificial intelligence for intraoperative guidance: Using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy. Ann. Surg. 276, 363–369 (2022).
doi: 10.1097/SLA.0000000000004594
pubmed: 33196488
Mascagni, P. et al. Artificial intelligence for surgical safety: Automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Ann. Surg. 275, 955–961 (2022).
doi: 10.1097/SLA.0000000000004351
pubmed: 33201104
Sato, K. et al. Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence. Surg. Endosc. 36, 5531–5539 (2022).
doi: 10.1007/s00464-022-09268-w
pubmed: 35476155
Ichinose, J., Matsuura, Y., Nakao, M. & Mun, M. A novel procedure of thoracoscopic 4L lymph node dissection: 4L posterior first technique. J. Vis. Surg. 6, 11 (2020).
doi: 10.21037/jovs.2019.11.12
Mun, M., Ichinose, J., Matsuura, Y., Nakao, M. & Okumura, S. Video-assisted thoracoscopic surgery lobectomy via confronting upside-down monitor setting. J. Vis. Surg. 3, 129 (2017).
doi: 10.21037/jovs.2017.07.08
pubmed: 29078689
pmcid: 5638999
Maier-Hein, L. et al. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9, 5217 (2018).
doi: 10.1038/s41467-018-07619-7
pubmed: 30523263
pmcid: 6284017