Application of multi ⁃ modal neuroimaging data information management system in functional neurosurgery
Abstract
Background Multi ‐ modal neuroimaging examinations play a crucial role in the diagnosis and treatment of functional neurosurgery. However, there is currently a lack of effective management for these complex data in clinical practice. This study attempts to establish a feasible multimodal neuroimaging data information management system and evaluate its application effects. Methods By standardizing clinical diagnosis and treatment processes, analyzing the nodes where imaging data were
generated, and streamlining data flow routes, establishing storage naming conventions, setting up storage servers, and training specialized personnel, we designed and applied a multi ‐ modal neuroimaging data information management system. The primary evaluation indicators were the archiving rates of 5 types of data: structural sequences, other preoperative images, postoperative electrode CT, electrode reconstruction, and postoperative CT/MRI. The secondary evaluation indicators included the total man‐hours consumed for data archiving and the average man‐hours consumed per case. Results Without multi‐modal neuroimaging data information management (control group, n = 64), the total manpower consumption was 192 man‐hours, with an average of 3 man‐hours per case. With multi‐modal neuroimaging data information management (data management group, n = 50), the total manpower consumption was 84 man‐hours, with an average of 1.68 man‐hours per case. The data management group had higher archiving rates compared to the control group: structural sequences [100% (50/50) vs. 32.81% (21/64); χ2 = 11.383, P = 0.001], other preoperative images [96% (48/50) vs. 26.56% (17/64); χ2 = 13.839, P = 0.000], postoperative electrode CT [96% (48/50) vs. 32.81% (21/64); χ2 = 10.409, P = 0.001], electrode reconstruction [96% (48/50) vs. 32.81% (21/64); χ2 = 10.409, P = 0.001], postoperative CT/MRI [96% (48/50) vs. 15.63% (10/64); χ2 = 22.169, P = 0.000]. Conclusions Designing a multi‐modal neuroimaging data information management system that aligns with clinical practice and reasonably setting data collection and archiving nodes can effectively improve data archiving rates, save manpower resources, ensure the complete storage of clinical data, and ensure the smooth operation of clinical tasks, and enhance clinical diagnosis and treatment levels.
DOI: 10.3969/j.issn.1672⁃6731.2024.07.005
generated, and streamlining data flow routes, establishing storage naming conventions, setting up storage servers, and training specialized personnel, we designed and applied a multi ‐ modal neuroimaging data information management system. The primary evaluation indicators were the archiving rates of 5 types of data: structural sequences, other preoperative images, postoperative electrode CT, electrode reconstruction, and postoperative CT/MRI. The secondary evaluation indicators included the total man‐hours consumed for data archiving and the average man‐hours consumed per case. Results Without multi‐modal neuroimaging data information management (control group, n = 64), the total manpower consumption was 192 man‐hours, with an average of 3 man‐hours per case. With multi‐modal neuroimaging data information management (data management group, n = 50), the total manpower consumption was 84 man‐hours, with an average of 1.68 man‐hours per case. The data management group had higher archiving rates compared to the control group: structural sequences [100% (50/50) vs. 32.81% (21/64); χ2 = 11.383, P = 0.001], other preoperative images [96% (48/50) vs. 26.56% (17/64); χ2 = 13.839, P = 0.000], postoperative electrode CT [96% (48/50) vs. 32.81% (21/64); χ2 = 10.409, P = 0.001], electrode reconstruction [96% (48/50) vs. 32.81% (21/64); χ2 = 10.409, P = 0.001], postoperative CT/MRI [96% (48/50) vs. 15.63% (10/64); χ2 = 22.169, P = 0.000]. Conclusions Designing a multi‐modal neuroimaging data information management system that aligns with clinical practice and reasonably setting data collection and archiving nodes can effectively improve data archiving rates, save manpower resources, ensure the complete storage of clinical data, and ensure the smooth operation of clinical tasks, and enhance clinical diagnosis and treatment levels.
DOI: 10.3969/j.issn.1672⁃6731.2024.07.005
Keywords
Neurosurgery; Neuroimaging; Electronic data processing; Health workforce
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