The Hox code responsible for the patterning of the anterior vertebrae in zebrafish.

The vertebral column is a characteristic structure of vertebrates. Genetic studies in mice have shown that Hox-mediated patterning plays a key role in specifying discrete anatomical regions of the vertebral column. Expression pattern analyses in several vertebrate embryos have provided correlative evidence that the anterior boundaries of Hox expression coincide with distinct anatomical vertebrae. However, because functional analyses have been limited to mice, it remains unclear which Hox genes actually function in vertebral patterning in other vertebrates. In this study, various zebrafish Hox mutants were generated for loss-of-function phenotypic analysis to functionally decipher the Hox code responsible for the zebrafish anterior vertebrae between the occipital and thoracic vertebrae. We found that Hox genes in HoxB- and HoxC-related clusters participate in regulating the morphology of the zebrafish anterior vertebrae. In addition, medaka hoxc6a was found to be responsible for anterior vertebral identity, as in zebrafish. Based on phenotypic similarities with Hoxc6 knockout mice, our results suggest that the Hox patterning system, including at least Hoxc6, may have been functionally established in the vertebral patterning of the common ancestor of ray-finned and lobe-finned fishes.

An atlas of seven zebrafish hox cluster mutants provides insights into sub/neofunctionalization of vertebrate Hox clusters.

Vertebrate Hox clusters are comprised of multiple Hox genes that control morphology and developmental timing along multiple body axes. Although results of genetic analyses using Hox-knockout mice have been accumulating, genetic studies in other vertebrates have not been sufficient for functional comparisons of vertebrate Hox genes. In this study, we isolated all of the seven hox cluster loss-of-function alleles in zebrafish using the CRISPR-Cas9 system. Comprehensive analysis of the embryonic phenotype and X-ray micro-computed tomography scan analysis of adult fish revealed several species-specific functional contributions of homologous Hox clusters along the appendicular axis, whereas important shared general principles were also confirmed, as exemplified by serial anterior vertebral transformations along the main body axis, observed in fish for the first time. Our results provide insights into discrete sub/neofunctionalization of vertebrate Hox clusters after quadruplication of the ancient Hox cluster. This set of seven complete hox cluster loss-of-function alleles provide a formidable resource for future developmental genetic analysis of the Hox patterning system in zebrafish.

Utility of Initial Tumor Reduction as a Prognostic Factor in Esophageal Squamous Cell Cancer Patients Undergoing Neoadjuvant Chemotherapy Followed by Surgery : A Retrospective Cohort Study.

BACKGROUND: While a neoadjuvant chemotherapy regimen using docetaxel, cisplatin, and 5-fluorouracil (NAC-DCF) is considered the standard treatment for locally advanced esophageal cancer (EC) in Japan, a reliable marker for early prediction of treatment efficacy remains unclear. We investigated the utility of the tumor response after a first course of NAC-DCF as a post-surgery survival predictor in patients with EC. METHODS: We enrolled 150 consecutive patients who underwent NAC-DCF followed by surgery for EC between September 2009 and January 2019. The initial tumor reduction (ITR), defined as the percentage decrease in the shorter diameter of the tumor after the first course of NAC-DCF, was evaluated using computed tomography. We analyzed the relationship between ITR, clinicopathological parameters, and survival. RESULTS: The median ITR was 21.07% (range -11.45 to 50.13%). The optimal cut-off value for ITR for predicting prognosis was 10% (hazard ratio [HR] 3.30, 95% confidence interval [CI] 1.98-5.51), based on univariate logistic regression analyses for recurrence-free survival (RFS). Compared with patients with ITR <10%, patients with ITR ≥10% showed a significantly higher proportion of ypM0 (80.0% vs. 92.5%) and responders in terms of overall clinical response (50.0% vs. 80.8%). Multivariate analysis for RFS revealed that ypN2-3 (HR 2.78, 95% CI 1.67-4.62), non-response in terms of overall clinical response (HR 1.87, 95% CI 1.10-3.18), and ITR <10% (HR 2.48, 95% CI 1.42-4.32) were independent prognostic factors. CONCLUSIONS: Tumor response after the first course of NAC-DCF may be a good predictor of survival in patients with EC who underwent NAC-DCF plus surgery.

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening.

This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs. PURPOSE: To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set. METHODS: A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated. RESULTS: The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship. CONCLUSION: Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

Incidental Findings in Trauma Patients: How Big is the Challenge?

INTRODUCTION: The purpose of this study was to examine the prevalence of incidental findings (IFs) identified during workup of trauma patients and the effectiveness with which they were documented and communicated. MATERIALS AND METHODS: We performed a retrospective analysis of all trauma patients ≥15 y of age in 2018, who underwent at least one computed tomography scan. Patients' Electronic Medical Record was reviewed for the presence of IFs. IFs were classified in three categories: category 1, which includes highly significant findings requiring attention during hospitalization; category 2, which warrants attention in an outpatient basis; and category 3, which includes nonsignificant findings that require no follow-up. RESULTS: 836 patients were identified, of which 582 had at least one IF. Of the patients with IFs; 14 (2.4%) were category 1, 138 (23.7%) were category 2, and 569 (97.8%) met category 3 criteria. All category 1 patients received appropriate documentation of their IFs. Of patients with category 2 findings, only 13% had documentation of the IFs. Patients with IFs had longer length of stay (P: 0.04) and lower probability of being discharged to home (P < 0.01) compared to patients with no IFs. Only 12.5% of the patients admitted to trauma surgery service received an outpatient follow-up. CONCLUSIONS: There was timely documentation and intervention for all patients with category 1 IFs. However, 87% of patients with category 2 IFs had inadequate documentation of the IF and outpatient follow-up. Outpatient follow-up of IFs poses a challenge for trauma patients partially due to their discharge disposition.

Accuracy of surgeons for predicting radiological sarcopenia in colorectal cancer surgery.

BACKGROUND: This study explored the performance of surgeons for predicting radiological sarcopenia as accessed by psoas cross-sectional area in patients with colorectal cancer (CRC). METHODS: A cross-sectional study was carried out and a diagnostic accuracy strategy was applied using the radiologist team assessment as gold standard. RESULTS: Cohort analysis of 45 consecutive patients found that 31.1% had sarcopenia. Correlation of Total Psoas Index between radiologists and surgeons was very strong for the Junior and strong for the Senior surgeon, with a strong correlation between the surgeons. By the simplistic criterion, agreement between radiologists and surgeons was substantial for both the Junior and Senior surgeons, with a moderate level between the surgeons. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of Junior surgeon were 92.9%, 83.9%, 72.2%, 96.3%, and 86.7%, respectively. The corresponding results for the Senior surgeon were 78.6%, 90.3%, 78.6%, 90.3%, and 86.7%, respectively. We found no major differences on agreement levels and performance of surgeons using the composite criterion. CONCLUSIONS: Surgeons seem to be accurate for identifying radiological sarcopenia in patients with CRC. The simplistic criterion should be preferred since a composite criterion adds complexity without increasing accuracy or agreement levels.

Imaging patterns of Lophomonas blattarum infection in the respiratory tract: a registry-based analysis.

BACKGROUND: Lophomonas blattarum is an emerging protozoan that mostly infects the lower respiratory tract and causes pulmonary lophomoniasis. Radiologic findings in patients with pulmonary lophomoniasis have yet to be studied. Thus, we conducted a registry-based clinical investigation to evaluate the radiologic findings of lophomoniasis. METHODS: In this cross-sectional study, 34 Lophomonas positive patients were enrolled. Demographic data, relevant characteristics, and radiologic findings of the patients were recorded and analyzed. RESULTS: Thirty-four (male = 18, female = 16) patients with an average age of 52.21 ± 20.48 years old were examined. Radiological findings such as Alveolar consolidation (26.5%), Ground glass opacity (5.9%), Centrilobular nodules (23.5%), Tree -in- bud (38.2%), Cavitation (23.5%), Pleural effusion (23.5%), Interstitial opacity (8.8%), Lymphadenopathy (23.5%), Bronchocele (5.9%), Bronchiectasis (29.4%), Nodules (8.8%) and Mass (11.8%) were obtained, that the frequency of all radiological findings was less than 50%. CONCLUSION: In this study, the most common radiological findings in patients with lophomoniasis were tree-in-bud nodules, alveolar consolidation, bronchiectasis, and centrilobular nodules which were mostly seen in the right lung and its middle and lower lobes. Given that the radiologic findings of this disease are unknown, it can be considered in differential diagnosis.

Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images.

BACKGROUND: This study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes. METHODS: Patients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations. RESULTS: Overall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR. CONCLUSIONS: Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.

Changes in thoracic radio density after living donor liver transplantation.

BACKGROUND: The outcomes after liver transplantation have greatly improved, which has resulted in greater focus on improving non-hepatic outcomes of liver transplantation. The present study aimed to evaluate thoracic spine radio density in children and adolescents after liver transplantation. METHODS: A total of 116 patients who underwent living donor liver transplantation were retrospectively analyzed. The radio density at the eleventh thoracic vertebra was measured using computed tomography scan performed preoperatively then annually for 5 years postoperatively and subsequently every 2 or 3 years. RESULTS: The mean thoracic radio density of male recipients of male grafts had the lowest values during the study. The radio density of patients receiving a graft from a female donor was higher than in recipients with grafts from males. Total mean radio density decreased for first 5 years postoperatively and then increased. Changes in radio density were equally distributed in both steroid withdrawal and no steroid withdrawal groups for 5 years, after which patients with steroid withdrawal had a greater increase. Changes in radio density were equally distributed in both the steroid withdrawal and no steroid withdrawal groups up to age 20, after which patients in the steroid withdrawal group had a greater increase. CONCLUSIONS: Gender differences may affect the outcome of radio density changes after transplantation. Given the moderate association between thoracic radio density and bone mineral density in skeletally mature adults and further studies are needed to validate this relationship between thoracic radio density and bone mineral density changes in pediatric liver transplantation.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images.

Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system's efficiency of detecting cancer and help the patient to get the appropriate treatment.

Volume X-Ray Micro-Computed Tomography Analysis of the Early Cephalized Central Nervous System in a Marine Flatworm, Stylochoplana pusilla.

Platyhelminthes are a phylum of simple bilaterian invertebrates with prototypic body systems. Compared with non-bilaterians such as cnidarians, the bilaterians are likely to exhibit integrated free-moving behaviors, which require a concentrated nervous system "brain" rather than the distributed nervous system of radiatans. Marine flatworms have an early cephalized 'central' nervous system compared not only with non-bilaterians but also with parasitic flatworms or freshwater planarians. In this study, we used the marine flatworm Stylochoplana pusilla as an excellent model organism in Platyhelminthes because of the early cephalized central nervous system. Here, we investigated the three-dimensional structures of the flatworm central nervous system by the use of X-ray micro-computed tomography (micro-CT) in a synchrotron radiation facility. We found that the obtained tomographic images were sufficient to discriminate some characteristic structures of the nervous system, including nerve cords around the cephalic ganglion, mushroom body-like structures, and putative optic nerves forming an optic commissure-like structure. Through the micro-CT imaging, we could obtain undistorted serial section images, permitting us to visualize precise spatial relationships of neuronal subpopulations and nerve tracts. 3-D micro-CT is very effective in the volume analysis of the nervous system at the cellular level; the methodology is straightforward and could be applied to many other non-model organisms.

eTEP Rives-Stoppa impact on abdominal contour: a retrospective observational and clinical quality improvement study using Ellipse 9 tool.

BACKGROUND: The eTEP Rives-Stoppa (RS) procedure, increasingly used for ventral hernia repair, has raised concerns about postoperative upper abdominal bulging. This study aims to objectively evaluate changes in the abdominal contour after eTEP RS and explore potential causes using a novel analytical tool, the Ellipse 9. METHODS: Thirty patients undergoing eTEP RS without posterior rectus sheath closure were assessed before and 3 months after surgery using CT scan images. Key measurements analyzed included the distance between linea semilunaris (X2), eccentricity over the Cord (c/a Cord), superior eccentricity (c/a Sup), Y2, and the superior perimeter of the abdomen. The Ellipse 9 tool, which provides graphical images and numerical representations, was utilized alongside patient-reported outcomes to assess perceived abdominal changes. RESULTS: The study group exhibited a trend toward a flatter abdomen with reduced distance between linea semilunaris(X2). However, 17% of patients developed upper abdominal bulging (5). Significant differences in c/a Cord, c/a Sup, Y2, and the superior perimeter of the abdomen, confirmed with Bonferroni corrections, were noted between bulging (5 patients) and non-bulging groups (25 patients). There was a notable disparity between patient perceptions and objective outcomes. CONCLUSION: The eTEP RS procedure improved abdominal contour in most patients from a selected cohort. The Ellipse 9 tool was valuable for the objective analysis of these changes. The cause of bulging post-eTEP RS is probably multifactorial. Notably, there was often a discrepancy between patient perceptions of bulging and objective clinical findings.

Diagnosis and treatment of epiploic appendagitis in a Middle Eastern country: An observational retrospective analysis of 156 cases.

BACKGROUND: Epiploic appendagitis (EPA) is an uncommon emergency surgical condition that causes acute abdominal pain, rendering a list of differential diagnoses. Therefore, careful examination and imaging tools are required. EPA is a self-limiting condition that can be resolved in 1-2 weeks and rarely needs surgical intervention. Its low incidence makes EPA less well-known among the public and some medical professionals, and it is frequently under-diagnosed. We aimed to explore the incidence, clinical presentation, modalities of imaging to diagnose and options for treating EPA. METHODS: An observational retrospective analysis was conducted between 2016 and 2022 at a tertiary hospital in an Arab Middle Eastern country. RESULTS: There were 156 EPA cases diagnosed over six years, with a mean age of 33 years. Males represented 82% of the cohort. The entire cohort was treated non-operatively except for eight patients who had surgical intervention using open or laparoscopic surgery. The diagnosis was made by a computerized tomographic scan (CT). However, plain X-ray, abdominal ultrasound, and magnetic resonance imaging (MRI) were performed initially in a few selected cases to rule out other conditions. No specific blood test indicated EPA; however, a histopathology examination was diagnostic. No mortality was reported in the study cohort. CONCLUSION: This is the most extensive study analyzing EPA patients from the Middle East. EPA is a rare and mostly self-limiting acute abdominal disorder; however, early ultrasound and CT scan can pick it up quickly after a high index of suspicion.

Diagnostic pathways in acute appendicitis: Clinical score plus ultrasound outperform CT scan or upfront surgery, especially in doubtful cases.

BACKGROUND: Controversies remain on the diagnostic strategy in suspected AA, considering the different settings worldwide. MATERIAL AND METHODS: A prospective observational international multicentric study including patients operated for suspected AA with a definitive histopathological analysis was conducted. Three groups were analyzed: (1) No radiology; (2) Ultrasound, and (3) Computed tomography. The aim was to analyze the performance of three diagnostic schemes. RESULTS: Three thousand and one hundred twenty three patients were enrolled; 899 in the no radiology group, 1490 in the US group, and 734 in the CT group. The sex ratio was in favor of males (p < 0.001). The mean age was lower in the no radiology group (24 years) compared to 28 and 38 years in US and CT-scan groups, respectively (p < 0.001). Overall, the negative appendectomy rate 3.8%: no radiology group (5.1%) versus US (2.9%) and CT-scan (4.1%) (p < 0.001). The sensitivity and specificity analysis showed the best balance in clinical evaluation + score + US. These data reach the best results in those patients with an equivocal Alvarado score (4-6). Inverse probability weighting (IPW), showed as the use of ultrasound, is significantly associated with an increased probability of formulating the correct diagnosis (p 0.004). In the case of a CT scan, this association appears weaker (p 0.08). CONCLUSION: The association of clinical scores and ultrasound seems the best strategy to reach a correct preoperative diagnosis in patients with clinical suspicion of AA, even in those population subgroups where the clinical score may have an equivocal result. This strategy can be especially useful in low-resource settings worldwide. CT-scan association may improve the detection of patients who may potentially be submitted to conservative treatment.

30-Day Risk Score for Mortality and Stroke in Patients with Carotid Artery Stenosis Using Artificial Intelligence Based Carotid Plaque Morphology.

OBJECTIVES: The gold standard for determining carotid artery stenosis intervention is based on a combination of percent stenosis and symptomatic status. Few studies have assessed plaque morphology as an additive tool for stroke prediction. Our goal was to create a predictive model and risk score for 30-day stroke and death inclusive of plaque morphology. METHODS: Patients with a CT angiography head/neck between 2010-2021 at a single institution and a diagnosis of carotid artery stenosis were included in our analysis. Each CT was used to create a 3D image of carotid plaque based off image recognition software. A stepwise backward regression was used to select variables for inclusion in our prediction models. Model discrimination was assessed with receiver operating characteristic curves (AUC). Additionally, calibration was performed and the model with the least Akaike Information Criterion (AIC) was selected. The risk score was modeled from the Framingham Study. Primary outcome was mortality/stroke. RESULTS: We created three models to predict mortality/stroke from 366 patients: model A using only clinical variables, model B using only plaque morphology and model C using both clinical and plaque morphology variables. Model A used age, sex, PAD, hyperlipidemia, BMI, COPD, and history of TIA/stroke and had an AUC of 0.737 and AIC of 285.4. Model B used perivascular adipose tissue volume, lumen area, calcified volume, and target lesion length and had an AUC of 0.644 and AIC of 304.8. Finally, model C combined both clinical and software variables of age, sex, matrix volume, history of TIA/stroke, BMI, perivascular adipose tissue, lipid rich necrotic core, COPD and hyperlipidemia and had an AUC of 0.759 and an AIC of 277.6. Model C was the most predictive because it had the highest AUC and lowest AIC. CONCLUSION: Our study demonstrates that combining both clinical factors and plaque morphology creates the best predication of a patient's risk for all-cause mortality or stroke from carotid artery stenosis. Additionally, we found that for patients with even 3 points in our risk score model have a 20% chance of stroke/death. Further prospective studies are needed to validate our findings.

Early laparoscopic cholecystectomy in acute mild gallstone pancreatitis. Is there a role for routine admission contrast-enhanced CT Scan?

PURPOSE: This study aims to evaluate the efficacy of admission contrast-enhanced CT scans in formulating strategies for performing early laparoscopic cholecystectomy in cases of acute gallstone pancreatitis. METHODS: Patients diagnosed with acute gallstone pancreatitis underwent a CT scan upon admission (after at least 24 h from symptom onset) to confirm diagnosis and assess peripancreatic fluid, collections, gallstones, and common bile duct stones. Patients with mild acute gallstone pancreatitis, following the Atlanta classification and Baltazar score A or B, were identified as candidates for early cholecystectomy (within 72 h of admission). RESULTS: Within the analyzed period, 272 patients were diagnosed with mild acute gallstone pancreatitis according to the Atlanta Guidelines. A total of 33 patients (12.1%) were excluded: 17 (6.25%) due to SIRS, 10 (3.6%) due to local complications identified in CT (Balthazar D/E), and 6 (2.2%) due to severe comorbidities. Enhanced CT scans accurately detected gallstones, common bile duct stones, pancreatic enlargement, inflammation, pancreatic collections, and peripancreatic fluid. Among the cohort, 239 patients were selected for early laparoscopic cholecystectomy. Routine intraoperative cholangiogram was conducted in all cases, and where choledocholithiasis was present, successful treatment occurred through common bile duct exploration. Only one case required conversion from laparoscopic to open surgery. There were no observed severe complications or mortality. CONCLUSION: Admission CT scans are instrumental in identifying clinically stable patients with local tomographic complications that contraindicate early surgery. Patients meeting the criteria for mild acute gallstone pancreatitis, as per Atlanta guidelines, without SIRS or local complications (Baltazar D/E), can safely undergo early cholecystectomy within the initial 72 h of admission.

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19.

INTRODUCTION: In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results. METHODS: We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters. RESULTS: Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively. CONCLUSIONS: These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.

BACKGROUND: Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer. METHODS: We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc. RESULTS: Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant. CONCLUSION: Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.

PulmoNet: a novel deep learning based pulmonary diseases detection model.

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, making the exchange of gas challenging for animals inhaling and exhaling. It varies from gentle and self-limiting such as the common cold and catarrh, to life-threatening ones, such as viral pneumonia (VP), bacterial pneumonia (BP), and tuberculosis, as well as a severe acute respiratory syndrome, such as the coronavirus 2019 (COVID-19). The cost of diagnosis and treatment of pulmonary infections is on the high side, most especially in developing countries, and since radiography images (X-ray and computed tomography (CT) scan images) have proven beneficial in detecting various pulmonary infections, many machine learning (ML) models and image processing procedures have been utilized to identify these infections. The need for timely and accurate detection can be lifesaving, especially during a pandemic. This paper, therefore, suggested a deep convolutional neural network (DCNN) founded image detection model, optimized with image augmentation technique, to detect three (3) different pulmonary diseases (COVID-19, bacterial pneumonia, and viral pneumonia). The dataset containing four (4) different classes (healthy (10,325), COVID-19 (3,749), BP (883), and VP (1,478)) was utilized as training/testing data for the suggested model. The model's performance indicates high potential in detecting the three (3) classes of pulmonary diseases. The model recorded average detection accuracy of 94%, 95.4%, 99.4%, and 98.30%, and training/detection time of about 60/50 s. This result indicates the proficiency of the suggested approach when likened to the traditional texture descriptors technique of pulmonary disease recognition utilizing X-ray and CT scan images. This study introduces an innovative deep convolutional neural network model to enhance the detection of pulmonary diseases like COVID-19 and pneumonia using radiography. This model, notable for its accuracy and efficiency, promises significant advancements in medical diagnostics, particularly beneficial in developing countries due to its potential to surpass traditional diagnostic methods.

Clinical application of high-resolution spiral CT scanning in the diagnosis of auriculotemporal and ossicle.

Precision and intelligence in evaluating the complexities of middle ear structures are required to diagnose auriculotemporal and ossicle-related diseases within otolaryngology. Due to the complexity of the anatomical details and the varied etiologies of illnesses such as trauma, chronic otitis media, and congenital anomalies, traditional diagnostic procedures may not yield accurate diagnoses. This research intends to enhance the diagnosis of diseases of the auriculotemporal region and ossicles by combining High-Resolution Spiral Computed Tomography (HRSCT) scanning with Deep Learning Techniques (DLT). This study employs a deep learning method, Convolutional Neural Network-UNet (CNN-UNet), to extract sub-pixel information from medical photos. This method equips doctors and researchers with cutting-edge resources, leading to groundbreaking discoveries and better patient healthcare. The research effort is the interaction between the CNN-UNet model and high-resolution Computed Tomography (CT) scans, automating activities including ossicle segmentation, fracture detection, and disruption cause classification, accelerating the diagnostic process and increasing clinical decision-making. The suggested HRSCT-DLT model represents the integration of high-resolution spiral CT scans with the CNN-UNet model, which has been fine-tuned to address the nuances of auriculotemporal and ossicular diseases. This novel combination improves diagnostic efficiency and our overall understanding of these intricate diseases. The results of this study highlight the promise of combining high-resolution CT scanning with the CNN-UNet model in otolaryngology, paving the way for more accurate diagnosis and more individualized treatment plans for patients experiencing auriculotemporal and ossicle-related disruptions.