ObjectiveTo investigate the clinical characteristics and risk factors of Mycoplasma pneumoniae pneumonia （MPP） in children based on a dual classification integrating imaging features and clinical severity. MethodsMedical records of 2 054 pediatric patients with MPP were retrospectively analyzed. The cohort was stratified into severe consolidation （n=253）， severe non-consolidation （n=118）， non-severe consolidation （n=393）， and non-severe non-consolidation groups （n=1 290） based on clinical and radiological findings. Inter group data and characteristics were compared and multiple regression analysis was conducted to construct a prediction model for severe consolidation group. ResultsSignificant differences were observed among the groups in terms of age， duration of fever， length of hospital stay， presence of pulmonary rales， inflammatory markers ［C-reactive protein （CRP） and lactate dehydrogenase （LDH）］， the use of hormones， and bronchoscopic treatment （all P < 0.05）. Compared with the severe non-consolidation group， non-severe consolidation group， and non-severe non-consolidation group， children in severe consolidation group exhibited the longest duration of fever ［8 （6， 11） days vs 6 （2， 9）， 7 （6， 9） and 6 （3， 8） days， respectively］ and the longest length of hospital stay ［7 （5， 8） days vs 6 （5， 8）， 6 （5， 8） and 6 （4， 7） days， respectively］. They also had the highest incidence of reduced breath sounds ［34 cases （13.4%） vs 2 cases （1.7%）， 29 cases （7.4%） and 13 cases （1.0%）， respectively］ and a substantially higher rate of coinfections， particularly viral infections ［63 cases （24.9%） vs 23 cases （19.5%）， 60 cases （15.3%） and 190 cases （14.7%）， respectively］. Multivariate analysis indicated that the independent risk factors for severe MPP （SMPP） were age > 4.5 years， length of hospital stay > 6.5 days， reduced breath sounds， neutrophil-to-lymphocyte ratio （NLR） > 1.66， LDH > 370.5 U/L， CRP > 9.5 mg/L， and coinfection with viruses. Reduced breath sounds （OR = 5.58， 95% CI： 2.45 - 12.69） and coinfection with bacteria （OR = 3.11， 95% CI： 1.43 - 6.75） were identified as the most significant risk factors for pulmonary consolidation in non-severe MPP children. Additionally， reduced breath sounds， coinfection with viruses， LDH > 365.5 U/L， and CRP > 32.1 mg/L were risk factors for severe pneumonia in children with pulmonary consolidation. For non-consolidation MPP children， the presence of pulmonary dry rales （OR = 2.28， 95% CI： 1.46 - 3.56） was the primary independent risk factor for the development of severe pneumonia. ConclusionThe chest imaging findings of MPP are associated with clinical severity， and the risk factor model constructed based on this imaging-clinical classification can assist in achieving precise hierarchical diagnosis and treatment in clinical practice.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.

The incidence of thyroid cancer has been increasing,and early diagnosis and treatment are crucial for improving patient prognosis.With the advancement of artificial intelligence(AI)technology,significant progress has been made in its application in the diagnosis and treatment of thyroid cancer.AI technology has notably enhanced the diagnostic accuracy of thyroid cancer.By optimizing imaging examinations such as ultrasound and CT scans,it can more precisely identify malignant features of thyroid nodules.In fine-needle aspiration biopsy,the integration of AI with genetic testing technologies has improved both the accuracy and efficiency of diagnosis.In terms of treatment,AI assists in intraoperative functional preservation,reducing the risk of surgical trauma.For instance,it can accurately identify the locations of the recurrent laryngeal nerve and parathyroid glands.Additionally,AI is capable of predicting the efficacy of 131I treatment and the risk of complications,thereby guiding postoperative follow-up and management.The core strength of AI technology lies in its powerful data processing and analytical capabilities,enabling it to uncover latent patterns within data and provide a scientific basis for treatment decision-making.Looking ahead,with continuous technological advancements,AI is expected to propel the diagnosis and treatment of thyroid cancer towards greater intelligence and precision.However,challenges such as data privacy and algorithm transparency need to be addressed.This article provides a review of the research progress of AI technology in the fields of diagnosis,treatment,and prognosis prediction of thyroid cancer,explores the current strengths and weaknesses of AI technology,and looks forward to its future development directions while acknowledging challenges like data privacy and algorithm transparency.