Predicting Invasiveness of Lung Adenocarcinoma at Chest CT with Deep Learning Ternary Classification Models.

Background Preoperative discrimination of preinvasive, minimally invasive, and invasive adenocarcinoma at CT informs clinical management decisions but may be challenging for classifying pure ground-glass nodules (pGGNs). Deep learning (DL) may improve ternary classification. Purpose To determine whether a strategy that includes an adjudication approach can enhance the performance of DL ternary classification models in predicting the invasiveness of adenocarcinoma at chest CT and maintain performance in classifying pGGNs. Materials and Methods In this retrospective study, six ternary models for classifying preinvasive, minimally invasive, and invasive adenocarcinoma were developed using a multicenter data set of lung nodules. The DL-based models were progressively modified through framework optimization, joint learning, and an adjudication strategy (simulating a multireader approach to resolving discordant nodule classifications), integrating two binary classification models with a ternary classification model to resolve discordant classifications sequentially. The six ternary models were then tested on an external data set of pGGNs imaged between December 2019 and January 2021. Diagnostic performance including accuracy, specificity, and sensitivity was assessed. The χ2 test was used to compare model performance in different subgroups stratified by clinical confounders. Results A total of 4929 nodules from 4483 patients (mean age, 50.1 years ± 9.5 [SD]; 2806 female) were divided into training (n = 3384), validation (n = 579), and internal (n = 966) test sets. A total of 361 pGGNs from 281 patients (mean age, 55.2 years ± 11.1 [SD]; 186 female) formed the external test set. The proposed strategy improved DL model performance in external testing (P < .001). For classifying minimally invasive adenocarcinoma, the accuracy was 85% and 79%, sensitivity was 75% and 63%, and specificity was 89% and 85% for the model with adjudication (model 6) and the model without (model 3), respectively. Model 6 showed a relatively narrow range (maximum minus minimum) across diagnostic indexes (accuracy, 1.7%; sensitivity, 7.3%; specificity, 0.9%) compared with the other models (accuracy, 0.6%-10.8%; sensitivity, 14%-39.1%; specificity, 5.5%-17.9%). Conclusion Combining framework optimization, joint learning, and an adjudication approach improved DL classification of adenocarcinoma invasiveness at chest CT. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Sohn and Fields in this issue.

LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images.

BACKGROUND: The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis. PURPOSE: To assess the lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios. MATERIAL AND METHODS: This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist. RESULTS: The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man-machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities. CONCLUSION: AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.

Mitochondrial One-Carbon Metabolism and Alzheimer's Disease.

Mitochondrial one-carbon metabolism provides carbon units to several pathways, including nucleic acid synthesis, mitochondrial metabolism, amino acid metabolism, and methylation reactions. Late-onset Alzheimer's disease is the most common age-related neurodegenerative disease, characterised by impaired energy metabolism, and is potentially linked to mitochondrial bioenergetics. Here, we discuss the intersection between the molecular pathways linked to both mitochondrial one-carbon metabolism and Alzheimer's disease. We propose that enhancing one-carbon metabolism could promote the metabolic processes that help brain cells cope with Alzheimer's disease-related injuries. We also highlight potential therapeutic avenues to leverage one-carbon metabolism to delay Alzheimer's disease pathology.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

CarveMix: A simple data augmentation method for brain lesion segmentation.

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed. These methods are easy to implement and have achieved promising results in various image processing tasks. However, existing data augmentation approaches based on image mixing are not designed for brain lesions and may not perform well for brain lesion segmentation. Thus, the design of this type of simple data augmentation method for brain lesion segmentation is still an open problem. In this work, we propose a simple yet effective data augmentation approach, dubbed as CarveMix, for CNN-based brain lesion segmentation. Like other mixing-based methods, CarveMix stochastically combines two existing annotated images (annotated for brain lesions only) to obtain new labeled samples. To make our method more suitable for brain lesion segmentation, CarveMix is lesion-aware, where the image combination is performed with a focus on the lesions and preserves the lesion information. Specifically, from one annotated image we carve a region of interest (ROI) according to the lesion location and geometry with a variable ROI size. The carved ROI then replaces the corresponding voxels in a second annotated image to synthesize new labeled images for network training, and additional harmonization steps are applied for heterogeneous data where the two annotated images can originate from different sources. Besides, we further propose to model the mass effect that is unique to whole brain tumor segmentation during image mixing. To evaluate the proposed method, experiments were performed on multiple publicly available or private datasets, and the results show that our method improves the accuracy of brain lesion segmentation. The code of the proposed method is available at https://github.com/ZhangxinruBIT/CarveMix.git.

A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer.

OBJECTIVES: To develop a pre-treatment CT-based predictive model to anticipate inoperable lung cancer patients' progression-free survival (PFS) to immunotherapy. METHODS: This single-center retrospective study developed and cross-validated a radiomic model in 185 patients and tested it in 48 patients. The binary endpoint is the durable clinical benefit (DCB, PFS ≥ 6 months) and non-DCB (NDCB, PFS < 6 months). Radiomic features were extracted from multiple intrapulmonary lesions and weighted by an attention-based multiple-instance learning model. Aggregated features were then selected through L2-regularized ridge regression. Five machine-learning classifiers were conducted to build predictive models using radiomic and clinical features alone and then together. Lastly, the predictive value of the model with the best performance was validated by Kaplan-Meier survival analysis. RESULTS: The predictive models based on the weighted radiomic approach showed superior performance across all classifiers (AUCs: 0.75-0.82) compared with the largest lesion approach (AUCs: 0.70-0.78) and the average sum approach (AUCs: 0.64-0.80). Among them, the logistic regression model yielded the most balanced performance (AUC = 0.87 [95%CI 0.84-0.89], 0.75 [0.68-0.82], 0.80 [0.68-0.92] in the training, validation, and test cohort respectively). The addition of five clinical characteristics significantly enhanced the performance of radiomic-only model (train: AUC 0.91 [0.89-0.93], p = .042; validation: AUC 0.86 [0.80-0.91], p = .011; test: AUC 0.86 [0.76-0.96], p = .026). Kaplan-Meier analysis of the radiomic-based predictive models showed a clear stratification between classifier-predicted DCB versus NDCB for PFS (HR = 2.40-2.95, p < 0.05). CONCLUSIONS: The adoption of weighted radiomic features from multiple intrapulmonary lesions has the potential to predict long-term PFS benefits for patients who are candidates for PD-1/PD-L1 immunotherapies. KEY POINTS: • Weighted radiomic-based model derived from multiple intrapulmonary lesions on pre-treatment CT images has the potential to predict durable clinical benefits of immunotherapy in lung cancer. • Early line immunotherapy is associated with longer progression-free survival in advanced lung cancer.

A multi-stage ensemble network system to diagnose adolescent idiopathic scoliosis.

OBJECTIVES: To develop a deep learning algorithm to automatically evaluate and diagnose scoliosis on full spinal X-ray images. METHODS: This retrospective study collected full spinal X-ray images (anteroposterior) from four hospital databases from January 1, 2018, to March 31, 2021. The data were divided into training and validation sets. Full spinal X-ray images for external validation were independently collected at one hospital from April 1, 2021, to June 30, 2021. Model effectiveness was validated with a public dataset. Statistical software R was used to analyze the accuracy and sensitivity of the model curvature and anatomical balance parameters and assess interrater consistency. RESULTS: This study included 788 and 185 training and test datasets, respectively. The accuracy and recall of the algorithm model for the Cobb angle, apical vertebrae (AV), upper vertebrae, and lower vertebrae were 89.36%, 85.71%, 77.2%, and 80.24% and 97.35%, 93.38%, 84.11%, and 87.42%, respectively. The symmetric mean absolute percentage error at the Cobb angle was 5.99%, and the automatic measurement time was 1.7 s. The mean absolute error values of the Cobb angle and the distances between the center sacral vertical line and AV and C7 plumb line were 1.07° and 1.12 and 1.38 mm, respectively. Statistical analysis confirmed that the Cobb angle results were in good agreement with the gold standard (interclass coefficients of 0.996, 0.978, and 0.825; p < 0.001). CONCLUSION: Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency. KEY POINTS: • Our deep learning algorithm model had high sensitivity and accuracy for scoliosis, which could help radiologists improve their diagnostic efficiency. • Multi-center validation data were used in this study to guarantee the reliability of the research. • Algorithmic model measures 200 times faster than radiologists.

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.

OBJECTIVE: To compare the performance of a deep learning (DL)-based method for diagnosing pulmonary nodules compared with radiologists' diagnostic approach in computed tomography (CT) of the chest. MATERIALS AND METHODS: A total of 150 pathologically confirmed pulmonary nodules (60% malignant) assessed and reported by radiologists were included. CT images were processed by the proposed DL-based method to generate the probability of malignancy (0-100%), and the nodules were divided into the groups of benign (0-39.9%), indeterminate (40.0-59.9%), and malignant (60.0-100%). Taking the pathological results as the gold standard, we compared the diagnostic performance of the proposed DL-based method with the radiologists' diagnostic approach using the McNemar-Bowker test. RESULTS: There was a statistically significant difference between the diagnosis results of the proposed DL-based method and the radiologists' diagnostic approach (p < 0.001). Moreover, there was no statistically significant difference in the composition of the diagnosis results between the proposed DL-based method and the radiologists' diagnostic approach (all p > 0.05). The difference in diagnostic accuracy between the proposed DL-based method (70%) and radiologists' diagnostic performance (64%) was not statistically significant (p = 0.243). CONCLUSIONS: The proposed DL-based method achieved an accuracy comparable with the radiologists' diagnostic approach in clinical practice. Furthermore, its advantage in improving diagnostic certainty may raise the radiologists' confidence in diagnosing pulmonary nodules and may help clinical management. Therefore, the proposed DL-based method showed great potential in a certain clinical application. KEY POINTS: • Deep learning-based method for diagnosing the pulmonary nodules in computed tomography provides a higher diagnostic certainty.

Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation. METHODS: Between June 2012 and June 2019, 95 resected SSNs with preoperative long-term follow-up were enrolled in this retrospective study. SSN detection and segmentation were performed on preoperative follow-up CTs using the deep learning-based Dr. Wise system. SSNs were categorized into invasive adenocarcinoma (IAC, n = 47) and non-IAC (n = 48) groups; according to the interval change during the preoperative follow-up, SSNs were divided into growth (n = 68), nongrowth (n = 22), and new emergence (n = 5) groups. We analyzed the cumulative percentages and pattern of SSN growth and identified significant factors for IAC diagnosis and SSN growth. RESULTS: The mean preoperative follow-up was 42.1 ± 17.0 months. More SSNs showed growth or new emergence in the IAC than in the non-IAC group (89.4% vs. 64.6%, p = 0.009). Volume doubling time was non-significantly shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days, p = 0.077). Median mass doubling time was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). Lobulated sign (p = 0.002) and SSN mass (p = 0.004) were significant factors for differentiating IACs. IACs showed significantly higher cumulative growth percentages than non-IACs in the first 70 months of follow-up. The growth pattern of SSNs may conform to the exponential model. The initial volume (p = 0.042) was a predictor for SSN growth. CONCLUSIONS: IACs appearing as SSNs showed an indolent course. The mean growth rate was larger for IACs than for non-IACs. SSNs with larger initial volume are more likely to grow. KEY POINTS: • Invasive adenocarcinomas (IACs) appearing as subsolid nodules (SSNs), with a mean volume doubling time (VDT) of 1436.0 ± 1188.2 days and median mass doubling time (MDT) of 821.7 days, showed an indolent course. • The VDT was shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days), but the difference was not significant (p = 0.077). The median MDT was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). • SSNs with lobulated sign and larger mass (> 390.5 mg) may very likely be IACs. SSNs with larger initial volume are more likely to grow.

Comparison of automated and manual DWI-ASPECTS in acute ischemic stroke: total and region-specific assessment.

OBJECTIVE: To compare the DWI-Alberta Stroke Program Early Computed Tomography Score calculated by a deep learning-based automatic software tool (eDWI-ASPECTS) with the neuroradiologists' evaluation for the acute stroke, with emphasis on its performance on 10 individual ASPECTS regions, and to determine the reasons for inconsistencies between eDWI-ASPECTS and neuroradiologists' evaluation. METHODS: This retrospective study included patients with middle cerebral artery stroke who underwent MRI from 2010 to 2019. All scans were evaluated by eDWI-ASPECTS and two independent neuroradiologists (with 15 and 5 years of experience in stroke study). Inter-rater agreement and agreement between manual vs. automated methods for total and each region were evaluated by calculating Kendall's tau-b, intraclass correlation coefficient (ICC), and kappa coefficient. RESULTS: In total, 309 patients met our study criteria. For total ASPECTS, eDWI-ASPECTS and manual raters had a strong positive correlation (Kendall's tau-b = 0.827 for junior raters vs. eDWI-ASPECTS; Kendall's tau-b = 0.870 for inter-raters; Kendall's tau-b = 0.848 for senior raters vs. eDWI-ASPECTS) and excellent agreement (ICC = 0.923 for junior raters and automated scores; ICC = 0.954 for inter-raters; ICC = 0.939 for senior raters and automated scores). Agreement was different for individual ASPECTS regions. All regions except for M5 region (κ = 0.216 for junior raters and automated scores), internal capsule (κ = 0.525 for junior raters and automated scores), and caudate (κ = 0.586 for senior raters and automated scores) showed good to excellent concordance. CONCLUSION: The eDWI-ASPECTS performed equally well as senior neuroradiologists' evaluation, although interference by uncertain scoring rules and midline shift resulted in poor to moderate consistency in the M5, internal capsule, and caudate nucleus regions. KEY POINTS: • The eDWI-ASPECTS based on deep learning perform equally well as senior neuroradiologists' evaluations. • Among the individual ASPECTS regions, the M5, internal capsule, and caudate regions mainly affected the overall consistency. • Uncertain scoring rules and midline shift are the main reasons for regional inconsistency.

Identification of pediatric respiratory diseases using a fine-grained diagnosis system.

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors' limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819. These results demonstrate that our proposed fine-grained diagnosis-assistant system provides precise identification of the diseases.

Combined Transcriptomic and Proteomic Analysis of Perk Toxicity Pathways.

In Drosophila, endoplasmic reticulum (ER) stress activates the protein kinase R-like endoplasmic reticulum kinase (dPerk). dPerk can also be activated by defective mitochondria in fly models of Parkinson's disease caused by mutations in pink1 or parkin. The Perk branch of the unfolded protein response (UPR) has emerged as a major toxic process in neurodegenerative disorders causing a chronic reduction in vital proteins and neuronal death. In this study, we combined microarray analysis and quantitative proteomics analysis in adult flies overexpressing dPerk to investigate the relationship between the transcriptional and translational response to dPerk activation. We identified tribbles and Heat shock protein 22 as two novel Drosophila activating transcription factor 4 (dAtf4) regulated transcripts. Using a combined bioinformatics tool kit, we demonstrated that the activation of dPerk leads to translational repression of mitochondrial proteins associated with glutathione and nucleotide metabolism, calcium signalling and iron-sulphur cluster biosynthesis. Further efforts to enhance these translationally repressed dPerk targets might offer protection against Perk toxicity.

Dynamic evolution of COVID-19 on chest computed tomography: experience from Jiangsu Province of China.

OBJECTIVES: To determine the patterns of chest computed tomography (CT) evolution according to disease severity in a large coronavirus disease 2019 (COVID-19) cohort in Jiangsu Province, China. METHODS: This retrospective cohort study was conducted from January 10, 2020, to February 18, 2020. All patients diagnosed with COVID-19 in Jiangsu Province were included, retrospectively. Quantitative CT measurements of pulmonary opacities including volume, density, and location were extracted by deep learning algorithm. Dynamic evolution of these measurements was investigated from symptom onset (day 1) to beyond day 15. Comparison was made between severity groups. RESULTS: A total of 484 patients (median age of 47 years, interquartile range 33-57) with 954 CT examinations were included, and each was assigned to one of the three groups: asymptomatic/mild (n = 63), moderate (n = 378), severe/critically ill (n = 43). Time series showed different evolution patterns of CT measurements in the groups. Following disease onset, posteroinferior subpleural area of the lung was the most common location for pulmonary opacities. Opacity volume continued to increase beyond 15 days in the severe/critically ill group, compared with peaking on days 13-15 in the moderate group. Asymptomatic/mild group had the lowest opacity volume which almost resolved after 15 days. The opacity density began to drop from day 10 to day 12 for moderately ill patients. CONCLUSIONS: Volume, density, and location of the pulmonary opacity and their evolution on CT varied with disease severity in COVID-19. These findings are valuable in understanding the nature of the disease and monitoring the patient's condition during the course of illness. KEY POINTS: • Volume, density, and location of the pulmonary opacity on CT change over time in COVID-19. • The evolution of CT appearance follows specific pattern, varying with disease severity.

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents. METHODS: A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score. RESULTS: Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance. CONCLUSIONS: The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists. KEY POINTS: • The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.

Reliability and Reproducibility of the Diameters Method in Rapid Determination of Acute Infarct Volume in Magnetic Resonance Diffusion-Weighted Imaging.

BACKGROUND: In acute ischemic stroke, diffusion-weighted imaging (DWI) volume is an independent predictive factor of poor outcome and an exclusion criterion for thrombolytic treatment. A simplified diameters method (ABC/2, orthogonal diameter [OD], and the maximum diameter [MD]) was proposed to replace the conventional measuring method and overcome the tedious and time-consuming defects, but its accuracy remains to be determined. OBJECTIVE: The objective of this study is to clarify the reliability and reproducibility of the diameter-based estimations in the infarct volume in DWI (Vol-DWI) measured by automated software. METHODS: Data of 316 patients with acute ischemic stroke who underwent MRI within 72 h at Jinling Hospital were retrospectively reviewed. Subgroup analysis by the location (cortex, white matter and deep gray nuclei, and combined) and volume (<70 and >70 mL) of cerebral infarction was evaluated. Relationship and consistency between the diameters methods and Vol-DWI were determined using Spearman rank correlation, Wilcoxon signed-rank test, and Bland-Altman plots. The OD and MD thresholds indicating infarct size >15, 70, and 100 mL were determined by generating receiver-operating characteristic (ROC) curves. Interobserver reliability was established using intraclass correlation coefficient and Bland-Altman plot. RESULTS: There was a strong positive correlation between the diameters and the Vol-DWI (ABC/2: r = 0.992, OD: r = 0.984, MD: r = 0.970, p < 0.001). Infarct volumes measured using the ABC/2 formula were significantly lower than those measured with Vol-DWI (Wilcoxon signed-rank test, z = 6.476, p < 0.001). Bland-Altman plot showed that the agreement of the volume <70 mL group, and white matter and deep gray nuclei groups was better than that of the other subgroups. For infarct volumes >15, 70, and 100 mL, the cutoff value for the MD was identified at 5, 6.9, and 8.4 cm, and the OD was identified at 12.47, 26.4, and 36.4 cm2, respectively, with a sensitivity and specificity >90%. CONCLUSIONS: The MD method was the best for achieving a rapid and excellent interobserver reliability for estimating infarct volume. Both OD and MD methods can quickly screen patients suitable for recanalization treatment and predict poor prognosis through threshold evaluation.

Tailoring steroids in the treatment of COVID-19 pneumonia assisted by CT scans: three case reports.

In this article, we analyze and report cases of three patients who were admitted to Renmin Hospital, Wuhan University, China, for treating COVID-19 pneumonia in February 2020 and were unresponsive to initial treatment of steroids. They were then received titrated steroids treatment based on the assessment of computed tomography (CT) images augmented and analyzed with the artificial intelligence (AI) tool and output. Three patients were finally recovered and discharged. The result indicated that sufficient steroids may be effective in treating the COVID-19 patients after frequent evaluation and timely adjustment according to the disease severity assessed based on the quantitative analysis of the images of serial CT scans.

Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.

This study aimed to assess the performance of a deep learning algorithm in helping radiologist achieve improved efficiency and accuracy in chest radiograph diagnosis. We adopted a deep learning algorithm to concurrently detect the presence of normal findings and 13 different abnormalities in chest radiographs and evaluated its performance in assisting radiologists. Each competing radiologist had to determine the presence or absence of these signs based on the label provided by the AI. The 100 radiographs were randomly divided into two sets for evaluation: one without AI assistance (control group) and one with AI assistance (test group). The accuracy, false-positive rate, false-negative rate, and analysis time of 111 radiologists (29 senior, 32 intermediate, and 50 junior) were evaluated. A radiologist was given an initial score of 14 points for each image read, with 1 point deducted for an incorrect answer and 0 points given for a correct answer. The final score for each doctor was automatically calculated by the backend calculator. We calculated the mean scores of each radiologist in the two groups (the control group and the test group) and calculated the mean scores to evaluate the performance of the radiologists with and without AI assistance. The average score of the 111 radiologists was 597 (587-605) in the control group and 619 (612-626) in the test group (P < 0.001). The time spent by the 111 radiologists on the control and test groups was 3279 (2972-3941) and 1926 (1710-2432) s, respectively (P < 0.001). The performance of the 111 radiologists in the two groups was evaluated by the area under the receiver operating characteristic curve (AUC). The radiologists showed better performance on the test group of radiographs in terms of normal findings, pulmonary fibrosis, heart shadow enlargement, mass, pleural effusion, and pulmonary consolidation recognition, with AUCs of 1.0, 0.950, 0.991, 1.0, 0.993, and 0.982, respectively. The radiologists alone showed better performance in aortic calcification (0.993), calcification (0.933), cavity (0.963), nodule (0.923), pleural thickening (0.957), and rib fracture (0.987) recognition. This competition verified the positive effects of deep learning methods in assisting radiologists in interpreting chest X-rays. AI assistance can help to improve both the efficacy and efficiency of radiologists.

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections.

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904-0.916) and 0.887 (95% CI: 0.867-0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805-0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775-0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.