Sufian Md Abu, Hamzi Wahiba, Sharifi Tazkera, Zaman Sadia, Alsadder Lujain, Lee Esther, Hakim Amir, Hamzi Boumediene
IVR Low-Carbon Research Institute, Chang'an University, Xi'an 710018, China.
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
J Pers Med. 2024 Aug 12;14(8):856. doi: 10.3390/jpm14080856.
Our research evaluates advanced artificial (AI) methodologies to enhance diagnostic accuracy in pulmonary radiography. Utilizing DenseNet121 and ResNet50, we analyzed 108,948 chest X-ray images from 32,717 patients and DenseNet121 achieved an area under the curve (AUC) of 94% in identifying the conditions of pneumothorax and oedema. The model's performance surpassed that of expert radiologists, though further improvements are necessary for diagnosing complex conditions such as emphysema, effusion, and hernia. Clinical validation integrating Latent Dirichlet Allocation (LDA) and Named Entity Recognition (NER) demonstrated the potential of natural language processing (NLP) in clinical workflows. The NER system achieved a precision of 92% and a recall of 88%. Sentiment analysis using DistilBERT provided a nuanced understanding of clinical notes, which is essential for refining diagnostic decisions. XGBoost and SHapley Additive exPlanations (SHAP) enhanced feature extraction and model interpretability. Local Interpretable Model-agnostic Explanations (LIME) and occlusion sensitivity analysis further enriched transparency, enabling healthcare providers to trust AI predictions. These AI techniques reduced processing times by 60% and annotation errors by 75%, setting a new benchmark for efficiency in thoracic diagnostics. The research explored the transformative potential of AI in medical imaging, advancing traditional diagnostics and accelerating medical evaluations in clinical settings.
我们的研究评估了先进的人工智能（AI）方法，以提高肺部X光摄影的诊断准确性。利用DenseNet121和ResNet50，我们分析了来自32717名患者的108948张胸部X光图像，DenseNet121在识别气胸和水肿情况时的曲线下面积（AUC）达到了94%。该模型的表现超过了放射科专家，不过对于诊断诸如肺气肿、积液和疝气等复杂病症，仍需进一步改进。整合潜在狄利克雷分配（LDA）和命名实体识别（NER）的临床验证证明了自然语言处理（NLP）在临床工作流程中的潜力。NER系统的精确率达到了92%，召回率为88%。使用DistilBERT进行的情感分析提供了对临床记录的细致理解，这对于完善诊断决策至关重要。XGBoost和SHapley加性解释（SHAP）增强了特征提取和模型可解释性。局部可解释模型无关解释（LIME）和遮挡敏感性分析进一步提高了透明度，使医疗服务提供者能够信任人工智能预测。这些人工智能技术将处理时间减少了60%，注释错误减少了75%，为胸部诊断的效率设定了新的基准。该研究探索了人工智能在医学成像中的变革潜力，推动了传统诊断方法，并加快了临床环境中的医学评估。
