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首页> 外文期刊>Medical Devices: Evidence and Research >The Machine Learned Stethoscope Provides Accurate Operator Independent Diagnosis of Chest Disease
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The Machine Learned Stethoscope Provides Accurate Operator Independent Diagnosis of Chest Disease

机译：机器学习听诊器提供精确的操作员独立诊断胸部病

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Introduction: Contemporary stethoscope has limitations in diagnosis of chest conditions, necessitating further imaging modalities. Methods: We created 2 diagnostic computer aided non-invasive machine-learning models to recognize chest sounds. Model A was interpreter independent based on hidden markov model and mel frequency cepstral coefficient (MFCC). Model B was based on MFCC, hidden markov model, and chest sound wave image interpreter dependent analysis (phonopulmonography (PPG)). Results: We studied 464 records of actual chest sounds belonging to 116 children diagnosed by clinicians and confirmed by other imaging diagnostic modalities. Model A had 96.7% overall correct classification rate (CCR), 100% sensitivity and 100% specificity in discrimination between normal and abnormal sounds. CCR was 100% for normal vesicular sounds, crepitations 89.1%, wheezes 97.6%, and bronchial breathing 100%. Model B’s CCR was 100% for normal vesicular sounds, crepitations 97.3%, wheezes 97.6%, and bronchial breathing 100%. The overall CCR was 98.7%, sensitivity and specificity were 100%. Conclusion: Both models demonstrated very high precision in the diagnosis of chest conditions and in differentiating normal from abnormal chest sounds irrespective of operator expertise. Incorporation of computer-aided models in stethoscopes promises prompt, precise, accurate, cost-effective, non-invasive, operator independent, objective diagnosis of chest conditions and reduces number of unnecessary imaging studies.
机译：简介：当代听诊器诊断胸部条件的局限性，需要进一步的成像方式。方法：我们创建了2个诊断计算机辅助非侵入机器学习模型，以识别胸部声音。模型A是基于隐马尔可夫模型和MEL频率谱系区（MFCC）独立的解释器。 B模型基于MFCC，隐马尔可夫模型和胸部声波图像解释器依赖性分析（致乐孔监测（PPG））。结果：我们研究了464次，属于临床医生诊断的116名儿童，并被其他成像诊断方式确认。模型A具有96.7％的总体正确分类率（CCR），100％敏感性和100％的鉴别在正常和异常的声音之间的特异性。 CCR为普通囊泡声音100％，裂缝89.1％，喘息97.6％，支气管呼吸100％。 B型CCR为普通囊泡声音100％，裂缝97.3％，喘息97.6％，支气管呼吸100％。整体CCR为98.7％，敏感性和特异性为100％。结论：两种型号在胸部条件的诊断中表现出非常高的精度，而且与操作员专业知识无关。在听诊器中加入计算机辅助模型，承诺提示，精确，准确，经济效益，无侵入性，操作员独立，客观诊断胸部条件，并减少了不必要的成像研究的数量。

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