摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肌肉骨骼疾病和条件的新研究-骨质疏松症是一篇报道的主题。据《新闻日报》记者从深圳发来的新闻报道，Resea Rch说，本研究旨在通过对(OP)骨质疏松患者肩袖撕裂(RCT)的x线定位肱骨近端密度(BMD)的测量和基于机器学习(ML)模型的临床资料的分析，对(OP)骨质疏松患者的肩袖撕裂进行诊断，并对其RCT严重程度进行分类。"包括63个OP和RCT(OPRCT),26个仅OP ."记者从广州中医药大学的研究中得到一句话，本研究分析了2021年4月至2023年4月的一系列肩关节X线片,根据肩关节AP X线片绘制ROIs后测量其灰度值,根据ROIs灰度值和临床资料(年龄、性别、AD优势侧、腰椎骨密度、骨密度SHAP值进一步分析显示，选择的特征对模型预测有显著影响，其中，SHAP值与BMD呈正相关，预测模型采用灰阶≥1-6、年龄、BMD、AM 9个变量，RF模型对OP患者RCT的有效诊断具有优势，AUC评分高达0.998、0.889、0.889.SHAP值显示，影响ROIs诊断结果的最主要因素是所有CAN细胞骨灰度值，建立了基于9个变量的柱线图预测模型，DCA曲线表明，基于该模型对OP患者的RCT预测更有利。RF模型预测OPRCT组内RCT类型的准确率最高,训练集和测试集的准确率分别为86.364%和73.684%,SHAP值表明影响预测结果的最显著因素是AM,其次是结节的灰度值。根据这九个变量，常规肩关节X线检查对OP患者RCT的发生和严重程度的诊断具有重要意义。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Musculoskeletal Diseas es and Conditions - Osteoporosis is the subject of a report. According to news r eporting from Shenzhen, People's Republic of China, by NewsRx journalists, resea rch stated, "This study aims to diagnose Rotator Cuff Tears (RCT) and classify t he severity of RCT in patients with Osteoporosis (OP) through the analysis of sh oulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mi neral density (BMD) measurements and clinical information based on machine learn ing (ML) models. A retrospective cohort of 89 patients was analyzed, including 6 3 with both OP and RCT (OPRCT) and 26 with OP only." The news correspondents obtained a quote from the research from the Guangzhou Un iversity of Chinese Medicine, "The study analyzed a series of shoulder radiograp hs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were develope d and compared based on their performance in predicting the occurrence and sever ity of RCT from ROIs' greyscale values and clinical information (age, gender, ad vantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model pre dictions. R1-6 had a positive correlation with BMD respectively. The nine variab les, including greyscale R1-6, age, BMD, and AM, were used in the prediction mod els. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, va lidation, and testing sets, respectively. SHAP values revealed that the most inf luential factors on the diagnostic outcomes were the grayscale values of all can cellous bones in ROIs. A column-line graph prediction model based on nine variab les was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most su perior in predicting the types of RCT within the OPRCT group, with an accuracy o f 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the grea ter tubercle, among others. ML models, particularly the RF algorithm, show signi ficant promise in diagnosing RCT occurrence and severity in OP patients using co nventional shoulder X-rays based on the nine variables."
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