Goswami Kartik K, Tak Nathaniel, Wadhawan Arnav, Landau Alec B, Bajaj Jashandeep, Sahni Jaskarn, Iqbal Zahid, Abedin Sami
College of Medicine, California Northstate University, Elk Grove, USA.
Medicine, Midwestern University Arizona College of Osteopathic Medicine, Glendale, USA.
Cureus. 2024 Jul 26;16(7):e65444. doi: 10.7759/cureus.65444. eCollection 2024 Jul.
Background The use of computational technology in medicine has allowed for an increase in the accuracy of clinical diagnosis, reducing errors through additional layers of oversight. Artificial intelligence technologies present the potential to further augment and expedite the accuracy, quality, and efficiency at which diagnosis can be made when used as an adjunctive tool. Such techniques, if found to be accurate and reliable in their diagnostic acuity, can be implemented to foster better clinical decision-making, improving patient quality of care while reducing healthcare costs. Methodology This study implemented convolution neural networks to develop a deep learning model capable of differentiating normal chest X-rays from those indicating pneumonia, tuberculosis, cardiomegaly, and COVID-19. There were 3,063 normal chest X-rays, 3,098 pneumonia chest X-rays, 2,920 COVID-19 chest X-rays, 2,214 chest X-rays, and 554 tuberculosis chest X-rays from Kaggle that were used for training and validation. The model was trained to recognize patterns within the chest X-rays to efficiently recognize these diseases within patients to be treated on time. Results The results indicated a success rate of 98.34% incorrect detections, exemplifying a high degree of accuracy. There are limitations to this study. Training models require hundreds to thousands of samples, and due to potential variability in image scanning equipment and techniques from which the images are sourced, the model could have learned to interpret external noise and unintended details which can adversely impact accuracy. Conclusions Further studies that implement more universal database-sourced images with similar image scanning techniques, assess diverse but related medical conditions, and the utilization of repeat trials can help assess the reliability of the model. These results highlight the potential of machine learning algorithms for disease detection with chest X-rays.
背景 医学中计算技术的应用提高了临床诊断的准确性，通过增加监督层次减少了错误。人工智能技术有潜力在用作辅助工具时进一步提高诊断的准确性、质量和效率。如果这些技术在诊断敏锐度方面被证明准确可靠，就可以实施以促进更好的临床决策，提高患者护理质量，同时降低医疗成本。
方法 本研究采用卷积神经网络开发了一种深度学习模型，能够区分正常胸部X光片与显示肺炎、肺结核、心脏肥大和新冠肺炎的X光片。从Kaggle获取了3063张正常胸部X光片、3098张肺炎胸部X光片、2920张新冠肺炎胸部X光片、2214张胸部X光片和554张肺结核胸部X光片用于训练和验证。该模型经过训练以识别胸部X光片中的模式，从而有效地及时识别待治疗患者体内的这些疾病。
结果 结果显示错误检测的成功率为98.34%，体现了高度的准确性。本研究存在局限性。训练模型需要数百到数千个样本，并且由于图像来源的扫描设备和技术可能存在差异，模型可能学会了解释外部噪声和意外细节，这可能对准确性产生不利影响。
结论 进一步的研究采用具有相似图像扫描技术的更通用数据库来源图像、评估多种但相关的医疗状况以及进行重复试验，有助于评估模型的可靠性。这些结果凸显了机器学习算法利用胸部X光片进行疾病检测的潜力。
