Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients
Abstract Background Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark ident...
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| Format: | Article |
| Language: | English |
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BMC
2021-10-01
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| Series: | BMC Anesthesiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12871-021-01466-8 |
| _version_ | 1831706438818332672 |
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| author | Jason Ju In Chan Jun Ma Yusong Leng Kok Kiong Tan Chin Wen Tan Rehena Sultana Alex Tiong Heng Sia Ban Leong Sng |
| author_facet | Jason Ju In Chan Jun Ma Yusong Leng Kok Kiong Tan Chin Wen Tan Rehena Sultana Alex Tiong Heng Sia Ban Leong Sng |
| author_sort | Jason Ju In Chan |
| collection | DOAJ |
| description | Abstract Background Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark identification program to assist anesthetists on spinal needle insertion point with a graphical user interface for spinal anesthesia. Methods Forty-eight obese patients requiring spinal anesthesia for Cesarean section were recruited in this prospective cohort study. We utilized a developed machine learning algorithm to determine the needle insertion point using automated spinal landmark ultrasound imaging of the lumbar spine identifying the L3/4 interspinous space (longitudinal view) and the posterior complex of dura mater (transverse view). The demographic and clinical characteristics were also recorded. Results The first attempt success rate for spinal anesthesia was 79.1% (38/48) (95%CI 65.0 - 89.5%), followed by successful second attempt of 12.5% (6/48), third attempt of 4.2% (2/48) and 4th attempt (4.2% or 2/48). The scanning duration of L3/4 interspinous space and the posterior complex were 21.0 [IQR: 17.0, 32.0] secs and 11.0 [IQR: 5.0, 22.0] secs respectively. There is good correlation between the program recorded depth of the skin to posterior complex and clinician measured depth (r = 0.915). Conclusions The automated spinal landmark identification program is able to provide assistance to needle insertion point identification in obese patients. There is good correlation between program recorded and clinician measured depth of the skin to posterior complex of dura mater. Future research may involve imaging algorithm improvement to assist with needle insertion guidance during neuraxial anesthesia. Trial registration This study was registered on clinicaltrials.gov registry ( NCT03687411 ) on 22 Aug 2018. |
| first_indexed | 2024-12-20T16:39:25Z |
| format | Article |
| id | doaj.art-4b97ab1192df434d8f6ed494b7f02207 |
| institution | Directory Open Access Journal |
| issn | 1471-2253 |
| language | English |
| last_indexed | 2024-12-20T16:39:25Z |
| publishDate | 2021-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Anesthesiology |
| spelling | doaj.art-4b97ab1192df434d8f6ed494b7f022072022-12-21T19:33:03ZengBMCBMC Anesthesiology1471-22532021-10-012111810.1186/s12871-021-01466-8Machine learning approach to needle insertion site identification for spinal anesthesia in obese patientsJason Ju In Chan0Jun Ma1Yusong Leng2Kok Kiong Tan3Chin Wen Tan4Rehena Sultana5Alex Tiong Heng Sia6Ban Leong Sng7Department of Women’s Anesthesia, KK Women’s and Children’s HospitalDepartment of Electrical and Computer Engineering, Faculty of Engineering, National University of SingaporeDepartment of Electrical and Computer Engineering, Faculty of Engineering, National University of SingaporeDepartment of Electrical and Computer Engineering, Faculty of Engineering, National University of SingaporeDepartment of Women’s Anesthesia, KK Women’s and Children’s HospitalCenter for Quantitative Medicine, Duke-NUS Medical SchoolDepartment of Women’s Anesthesia, KK Women’s and Children’s HospitalDepartment of Women’s Anesthesia, KK Women’s and Children’s HospitalAbstract Background Ultrasonography for neuraxial anesthesia is increasingly being used to identify spinal structures and the identification of correct point of needle insertion to improve procedural success, in particular in obesity. We developed an ultrasound-guided automated spinal landmark identification program to assist anesthetists on spinal needle insertion point with a graphical user interface for spinal anesthesia. Methods Forty-eight obese patients requiring spinal anesthesia for Cesarean section were recruited in this prospective cohort study. We utilized a developed machine learning algorithm to determine the needle insertion point using automated spinal landmark ultrasound imaging of the lumbar spine identifying the L3/4 interspinous space (longitudinal view) and the posterior complex of dura mater (transverse view). The demographic and clinical characteristics were also recorded. Results The first attempt success rate for spinal anesthesia was 79.1% (38/48) (95%CI 65.0 - 89.5%), followed by successful second attempt of 12.5% (6/48), third attempt of 4.2% (2/48) and 4th attempt (4.2% or 2/48). The scanning duration of L3/4 interspinous space and the posterior complex were 21.0 [IQR: 17.0, 32.0] secs and 11.0 [IQR: 5.0, 22.0] secs respectively. There is good correlation between the program recorded depth of the skin to posterior complex and clinician measured depth (r = 0.915). Conclusions The automated spinal landmark identification program is able to provide assistance to needle insertion point identification in obese patients. There is good correlation between program recorded and clinician measured depth of the skin to posterior complex of dura mater. Future research may involve imaging algorithm improvement to assist with needle insertion guidance during neuraxial anesthesia. Trial registration This study was registered on clinicaltrials.gov registry ( NCT03687411 ) on 22 Aug 2018.https://doi.org/10.1186/s12871-021-01466-8Neuraxial anesthesiaSpinalAutomatedUltrasound |
| spellingShingle | Jason Ju In Chan Jun Ma Yusong Leng Kok Kiong Tan Chin Wen Tan Rehena Sultana Alex Tiong Heng Sia Ban Leong Sng Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients BMC Anesthesiology Neuraxial anesthesia Spinal Automated Ultrasound |
| title | Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| title_full | Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| title_fullStr | Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| title_full_unstemmed | Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| title_short | Machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| title_sort | machine learning approach to needle insertion site identification for spinal anesthesia in obese patients |
| topic | Neuraxial anesthesia Spinal Automated Ultrasound |
| url | https://doi.org/10.1186/s12871-021-01466-8 |
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