Objective The identification of TCM constitution plays an important role in"treating and preventing diseases"of TCM.At present,the identification of damp-heat constitution and balanced constitution is mostly determined by questionnaire,and subjective factors have a great influence.Aiming at the identification of damp-heat constitution and balanced constitution in TCM,this paper utilizes voice signal to automatically realize the constitution identification task,in order to provide assistance for the clinical identification of TCM constitution.Methods Based on deep learning Transformer and transfer learning,a pure attentional mechanism model was designed for the identification of constitution in TCM sound diagnosis.We collected 700 voices from 34 subjects,pre-processed the voice data to obtain the corresponding Mayer spectrum diagram,and used the Transformer model pre-trained based on the public data set to improve the performance of the model for audio classification.Results The accuracy of the experimental results was 83.33%,the AUC was 92.16%,the sensitivity was 80.25%,and the specificity was 87.03%.Compared with the Convolutional Neural Network(CNN),the performance of the deep learning model was better.Conclusion In this paper,the damp-heat constitution and balanced constitution identification model Transformer has achieved better identification effect,indicating that it can improve the efficiency of TCM acoustic diagnosis of constitution identification,and promote the objective and intelligent development of constitution identification.

Objective This study investigated feasible cases and their significance in promoting the"generalized"development of inspection through digital-intelligent equipment.Methods A qualitative research approach was used,involving interviews conducted between February 2025 and March 2025 with experts in traditional Chinese medicine diagnostics,clinical research methodology,medical engineering integration,and related disciplines,using both online and offline methods.In accordance with the Consolidated Criteria for Reporting Qualitative Research,feasible cases involving the specific application of digital equipment in various parts of observation were collected through item enrichment.The significance of extending observation capabilities via these cases was analyzed,along with the overall implications of integrating digital technologies with traditional inspection method.Results Interviews were completed with 11 experts from domestic universities and research institutes in the fields of traditional Chinese medicine diagnosis,medical engineering integration,and related disciplines.A total of 78 feasible cases of digital-intelligent inspection were identified,along with 69 insights regarding the significance of enhancing the inspection capabilities.These insights were synthesized into two dimensions and 23 holistic meanings.The first dimension is to expand the scope of inspection,including obtaining internal environmental characteristics,observing external environmental characteristics,expanding thermodynamic characteristic data,and crossing time and space.The second dimension is to improve the quality of observation and diagnosis information collection and analysis,including 19 specific meanings,such as standardized collection environment,objective quantification,and refined observation.Conclusion Digital-intelligent equipment plays a significant role in expanding the scope of inspection content and achieving high-quality acquisition and analysis of extensive inspection information.These advancements extend and enrich the capabilities of traditional inspection method in traditional Chinese medicine.

Objective This study investigated feasible cases and their significance in promoting the"generalized"development of inspection through digital-intelligent equipment.Methods A qualitative research approach was used,involving interviews conducted between February 2025 and March 2025 with experts in traditional Chinese medicine diagnostics,clinical research methodology,medical engineering integration,and related disciplines,using both online and offline methods.In accordance with the Consolidated Criteria for Reporting Qualitative Research,feasible cases involving the specific application of digital equipment in various parts of observation were collected through item enrichment.The significance of extending observation capabilities via these cases was analyzed,along with the overall implications of integrating digital technologies with traditional inspection method.Results Interviews were completed with 11 experts from domestic universities and research institutes in the fields of traditional Chinese medicine diagnosis,medical engineering integration,and related disciplines.A total of 78 feasible cases of digital-intelligent inspection were identified,along with 69 insights regarding the significance of enhancing the inspection capabilities.These insights were synthesized into two dimensions and 23 holistic meanings.The first dimension is to expand the scope of inspection,including obtaining internal environmental characteristics,observing external environmental characteristics,expanding thermodynamic characteristic data,and crossing time and space.The second dimension is to improve the quality of observation and diagnosis information collection and analysis,including 19 specific meanings,such as standardized collection environment,objective quantification,and refined observation.Conclusion Digital-intelligent equipment plays a significant role in expanding the scope of inspection content and achieving high-quality acquisition and analysis of extensive inspection information.These advancements extend and enrich the capabilities of traditional inspection method in traditional Chinese medicine.

Objective The identification of TCM constitution plays an important role in"treating and preventing diseases"of TCM.At present,the identification of damp-heat constitution and balanced constitution is mostly determined by questionnaire,and subjective factors have a great influence.Aiming at the identification of damp-heat constitution and balanced constitution in TCM,this paper utilizes voice signal to automatically realize the constitution identification task,in order to provide assistance for the clinical identification of TCM constitution.Methods Based on deep learning Transformer and transfer learning,a pure attentional mechanism model was designed for the identification of constitution in TCM sound diagnosis.We collected 700 voices from 34 subjects,pre-processed the voice data to obtain the corresponding Mayer spectrum diagram,and used the Transformer model pre-trained based on the public data set to improve the performance of the model for audio classification.Results The accuracy of the experimental results was 83.33%,the AUC was 92.16%,the sensitivity was 80.25%,and the specificity was 87.03%.Compared with the Convolutional Neural Network(CNN),the performance of the deep learning model was better.Conclusion In this paper,the damp-heat constitution and balanced constitution identification model Transformer has achieved better identification effect,indicating that it can improve the efficiency of TCM acoustic diagnosis of constitution identification,and promote the objective and intelligent development of constitution identification.