ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
The early diagnosis of lung cancer and the corresponding treatment measures are crucial factors to reduce mortality rate. As an emerging technology, artificial intelligence has developed rapidly and it is used in the medical field to provide new ideas for the early diagnosis of lung cancer, which has achieved remarkable results. Artificial intelligence greatly eases the pressure of clinical work, changes the current medical model, and is expected to make doctors as a decision-maker. This article mainly describes the research progress on artificial intelligence in the identification of benign and malignant lung nodules, pathological typing, determination of markers, and detection of plasma circulating tumor DNA.
ObjectiveTo analyze the predictive value of ensemble classification algorithm of random forest for delirium risk in ICU patients with cardiothoracic surgery. MethodsA total of 360 patients hospitalized in cardiothoracic ICU of our hospital from June 2019 to December 2020 were retrospectively analyzed. There were 193 males and 167 females, aged 18-80 (56.45±9.33) years. The patients were divided into a delirium group and a control group according to whether delirium occurred during hospitalization or not. The clinical data of the two groups were compared, and the related factors affecting the occurrence of delirium in cardiothoracic ICU patients were predicted by the multivariate logistic regression analysis and the ensemble classification algorithm of random forest respectively, and the difference of the prediction efficiency between the two groups was compared.ResultsOf the included patients, 19 patients fell out, 165 patients developed ICU delirium and were enrolled into the delirium group, with an incidence of 48.39% in ICU, and the remaining 176 patients without ICU delirium were enrolled into the control group. There was no statistical significance in gender, educational level, or other general data between the two groups (P>0.05). But compared with the control group, the patients of the delirium group were older, length of hospital stay was longer, and acute physiology and chronic health evaluationⅡ(APACHEⅡ) score, proportion of mechanical assisted ventilation, physical constraints, sedative drug use in the delirium group were higher (P<0.05). Multivariate logistic regression analysis showed that age (OR=1.162), length of hospital stay (OR=1.238), APACHEⅡ score (OR=1.057), mechanical ventilation (OR=1.329), physical constraints (OR=1.345) and sedative drug use (OR=1.630) were independent risk factors for delirium of cardiothoracic ICU patients. The variables in the random forest model for sorting, on top of important predictor variable were: age, length of hospital stay, APACHEⅡ score, mechanical ventilation, physical constraints and sedative drug use. The diagnostic efficiency of ensemble classification algorithm of random forest was obviously higher than that of multivariate logistic regression analysis. The area under receiver operating characteristic curve of ensemble classification algorithm of random forest was 0.87, and the one of multivariate logistic regression analysis model was 0.79.ConclusionThe ensemble classification algorithm of random forest is more effective in predicting the occurrence of delirium in cardiothoracic ICU patients, which can be popularized and applied in clinical practice and contribute to early identification and strengthening nursing of high-risk patients.
The incidence of lung cancer has increased significantly during the past decades. Pathology is the gold standard for diagnosis and the corresponding treatment measures selection of lung cancer. In recent years, with the development of artificial intelligence and digital pathology, the researches of pathological image analysis have achieved remarkable progresses in lung cancer. In this review, we will introduce the research progress on artificial intelligence in pathological classification, mutation genes and prognosis of lung cancer. Artificial intelligence is expected to further accelerate the pace of precision pathology.
Objective To explore the use of ChatGPT (Chat Generative Pre-trained Transformer) in pediatric diagnosis, treatment and doctor-patient communication, evaluate the professionalism and accuracy of the medical advice provided, and assess its ability to provide psychological support. Methods The knowledge databases of ChatGPT 3.5 and 4.0 versions as of April 2023 were selected. A total of 30 diagnosis and treatment questions and 10 doctor-patient communication questions regarding the pediatric urinary system were submitted to ChatGPT versions 3.5 and 4.0, and the answers to ChatGPT were evaluated. Results The answers to the 40 questions answered by ChatGPT versions 3.5 and 4.0 all reached the qualified level. The answers to 30 diagnostic and treatment questions in ChatGPT 4.0 version were superior to those in ChatGPT 3.5 version (P=0.024). There was no statistically significant difference in the answers to the 10 doctor-patient communication questions answered by ChatGPT 3.5 and 4.0 versions (P=0.727). For prevention, single symptom, and disease diagnosis and treatment questions, ChatGPT’s answer scores were relatively high. For questions related to the diagnosis and treatment of complex medical conditions, ChatGPT’s answer scores were relatively low. Conclusion ChatGPT has certain value in assisting pediatric diagnosis, treatment and doctor-patient communication, but the medical advice provided by ChatGPT cannot completely replace the professional judgment and personal care of doctors.
ObjectiveTo explore the application of Tsetlin Machine (TM) in heart beat classification. MethodsTM was used to classify the normal beats, premature ventricular contraction (PVC) and supraventricular premature beats (SPB) in the 2020 data set of China Physiological Signal Challenge. This data set consisted of the single-lead electrocardiogram data of 10 patients with arrhythmia. One patient with atrial fibrillation was excluded, and finally data of the other 9 patients were included in this study. The classification results were then analyzed. ResultsThe classification results showed that the average recognition accuracy of TM was 84.3%, and the basis of classification could be shown by the bit pattern interpretation diagram. ConclusionTM can explain the classification results when classifying heart beats. The reasonable interpretation of classification results can increase the reliability of the model and facilitate people's review and understanding.
The coming out of electromagnetic navigation bronchoscopy gives exciting solution for diagnosis and even treatment of peripheral pulmonary nodules. It breaks the barriers of traditional bronchoscopy, and gives live visible imaging guidance for operators during biopsy of peripheral pulmonary nodules. The electromagnetic navigation bronchoscopy system can intelligently recognize and reconstruct the bronchial tree of the patients, and generate visible data and virtual guidance for the operators. It can perceive real-time magnetic localization of the signal, so as to precisely guide the navigational or biopsy tools. This review introduced the artificial intelligence configuration of the electromagnetic navigation bronchoscopy system based on the Veran system, and gave some improvement advices based on the defects of the system. In this way, we hope to promote the development and better clinical application of electromagnetic navigation bronchoscopy system.
ObjectiveTo summarize the current research progress in the prediction of the efficacy of neoadjuvant therapy of breast cancer based on the application of artificial intelligence (AI) and radiomics. MethodThe researches on the application of AI and radiomics in neoadjuvant therapy of breast cancer in recent 5 years at home and abroad were searched in CNKI, Google Scholar, Wanfang database and PubMed database, and the related research progress was reviewed. ResultsAI had developed rapidly in the field of medical imaging, and molybdenum target, ultrasound and magnetic resonance imaging combined with AI had been deepened and expanded in different degrees in the application research of breast cancer diagnosis and treatment. In the research of molybdenum target combined with AI, the high sensitivity of molybdenum target to microcalcification was mostly used to improve the accuracy of early detection and diagnosis of breast cancer, so as to achieve the clinical purpose of early detection and diagnosis. However, in terms of prediction of neoadjuvant efficacy research of breast cancer, ultrasound and magnetic resonance imaging combined with AI were more prevalent, and their popularity remained unabated. ConclusionIn the monitoring of neoadjuvant therapy for breast cancer, the use of properly designed AI and radiomics models can give full play to its role in the predicting the curative effect of neoadjuvant therapy, and help to guide doctors in clinical diagnosis and treatment and evaluate the prognosis of breast cancer patients.
ObjectiveTo analyze the incidence and drainage pattern of the specific collateral vein (VL) located between several adjacent segments of the bilateral upper lung, and its clinical significance in the surgical treatment of early lung cancer. MethodsThe preoperative three-dimensional computed tomography bronchography and angiography (3D CTBA) data of 1 515 patients in the First Affiliated Hospital of Nanjing Medical University from 2017 to 2020 were analyzed retrospectively, including 524 males and 991 females, with an average age of 54.27±11.43 years. There were 712 patients of right upper lung and 803 patients of left upper lung. The incidence and drainage pattern of VL in bilateral upper lungs were analyzed. Furthermore, the imaging data and medical records of 113 patients in the left upper lung were reviewed to investigate the influence of the relative position relationship between nodules and VL on the selection of operation. ResultsThe overall incidence of VL was 72.7% (1102/1 515) in the bilateral upper lungs, including 68.0% (484/712) in the right upper lung, and 77.0% (618/803) in the left upper lung. The incidence of VL in the left side was significantly higher than that in the right side (P＜0. 05). VL mainly drained into V2a+b (327/484, 67.6%) in the right upper lung and into V1+2b+c (389/618, 62.9%) in the left upper lung. When the spherical simulative cutting margin of 2 cm of the nodule did not involve VL, it was more feasible to undergo sublobectomy than those whose simulative cutting margin of 2 cm involved VL, and the difference was statistically significant (91.9% vs. 61.5%, P＜0.05). When the spherical simulative cutting margin of 2 cm of nodule involved VL, the lesion located in the middle or inner zone was more feasible to undergo lobectomy than that in the outer zone, but the difference was not statistically significant (43.8% vs. 34.8%, P＞0.05). Multivariate logistic regression analysis showed that diameter of the lesion, whether the spherical simulative margin of 2 cm involving VL and the depth ratio of the lesion were independent risk factors affecting the surgical options (P<0.05). ConclusionThe incidence of the specific collateral vein in bilateral upper lungs is high, and the drainage pattern is diverse, which has important guiding significance for preoperative planning and intraoperative manipulation. For deep nodules adjacent to VL, lobectomy or resection of left upper division is often performed to ensure a safe margin.
Coronary heart disease is the second leading cause of death worldwide. As a preventable and treatable chronic disease, early screening is of great importance for disease control. However, previous screening tools relied on physician assistance, thus cannot be used on a large scale. Many facial features have been reported to be associated with coronary heart disease and may be useful for screening. However, these facial features have limitations such as fewer types, irregular definitions and poor repeatability of manual judgment, so they can not be routinely applied in clinical practice. With the development of artificial intelligence, it is possible to integrate facial features to predict diseases. A recent study published in the European Heart Journal showed that coronary heart disease can be predicted using artificial intelligence based on facial photos. Although this work still has some limitations, this novel technology will be promise for improving disease screening and diagnosis in the future.