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Pneumothorax

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To collect the CXR data for retrospective study, we addressed a municipal hospital in the Czech Republic that provides healthcare services to up to 130,000 residents of a medium-sized city (approximately 70,000 inhabitants) and the surrounding area. 127 anonymized CXR images were collected between August 15 and 17, 2022, and subsequently submitted to five independent radiologists of varying experience for annotation. The selected radiologists were asked to assess whether the CXR image shows any of the 12 abnormalities mentioned above. Pediatric CXR images (under 18 years of age), scans with technical problems (poor image quality, rotation), and images in lateral projection were excluded from the dataset.

Carebot AI CXR is a deep learning-based software that assists radiologists in the interpretation of chest radiographs in posterior-anterior (PA) or anterior-posterior (AP) projections. The solution with artificial intelligence automatically detects abnormality based on visual patterns for the following findings: atelectasis, consolidation, cardiomegaly, mediastinal widening, pneumoperitoneum, pneumothorax, pulmonary edema, pulmonary lesion, bone fracture, hilar enlargement, subcutaneous emphysema, and pleural effusion.

Carebot AI CXR

The purpose of this study is to describe the design, methodology and evaluation of the preclinical test of Carebot AI CXR software, and to provide evidence that the investigated medical device meets user requirements in accordance with its intended use. Carebot AI CXR is defined as a recommendation system (classification "prediction") based on computer-aided detection. The software can be used in a preclinical deployment at a selected site before interpretation (prioritization, display of all results and heatmaps) or after interpretation (verification of findings) of CXR images, and in accordance with the manufacturer's recommendations. Given this, a retrospective study is performed to test the clinical effectiveness on existing CXRs.

The performance of the trained and internally validated Carebot AI CXR software is tested on a set of 127 CXR images from target population. This is compared to common clinical practice, i.e., image assessment by a radiologist in a hospital. Patients may have a variety of findings; at this stage of the evaluation, an abnormal finding is considered to be an abnormality in any of the defined classes. False negative images incorrectly predicted by Carebot AI CXR software result in a clinical impact determination.

To collect the CXR data for retrospective study, investigators addressed a municipal hospital in the Czech Republic that provides healthcare services to up to 130,000 residents of a medium-sized city (approximately 70,000 inhabitants) and the surrounding area. 127 anonymized CXR images were collected between August 15 and 17, 2022, and subsequently submitted to five independent radiologists of varying experience for annotation. The selected radiologists were asked to assess whether the CXR image shows any of the 12 pre-selected abnormalities. Pediatric CXR images (under 18 years of age), scans with technical problems (poor image quality, rotation), and images in lateral projection were excluded from the dataset.

Retrospective Study of Carebot AI CXR Performance in Preclinical Practice

Chest X-Ray Abnormality Detection Using Artificial Intelligence: Retrospective Study of Carebot AI CXR Performance in Preclinical Practice

The results of the retrospective study are used to validate the Carebot AI CXR software for the target population prior to deployment in the workplace.

Comparison of the accuracy of radiologist and Carebot AI CXR image assessment.

Comparison of the accuracy of radiologis with different experience vs. Carebot AI CXR. Weakness assessment of Carebot AI CXR.

Carebot s.r.o.

Digestive System Diseases

Rare Diseases

Emergencies

Fibrosis

Multiple Pulmonary Nodules

Pneumoperitoneum

Pulmonary Fibrosis

Solitary Pulmonary Nodule

Respiratory Tract Neoplasms

Thoracic Neoplasms

Peritoneal Diseases

Gastrointestinal Diseases

Lung Diseases, Interstitial

Chronic Graft Versus Host Disease

Acute Graft Versus Host Disease

Reader Selection: 30 readers will be selected from the following five clinical specialty groups:

* emergency medicine (ED)
* adult intensive care (ICU)
* adult general medicine (AGM)
* radiographers (Rad)
* general radiologists

Each specialty group consists of 6 members of ranked seniority. For the physicians this consists of:

* Two 'Juniors' (Foundation Year 1 - Specialty Training 2 years)
* Two 'Middle Grades' (Registrar from Specialty Training 3 to 6 years)
* Two Consultants

For the radiographers, this consists of:

* Two 'Junior/Newly qualified radiographers' (up to 18 months experience post qualification)
* Two 'Mid-experience radiographers' (approx. 3 years' experience)
* Two 'Reporting radiographers' (5+ years' experience)

Two consultant thoracic radiologists. A third senior thoracic radiologist's opinion (\>20 years experience) will undertake arbitration.

The reading will be done remotely via the Report and Image Quality Control site (www.RAIQC.com), an online platform allowing medical imaging viewing and reporting. Participants can work from any location, but the work must be done from a computer with internet access. For avoidance of doubt, the work cannot be performed from a phone or tablet.

The project is divided into two phases and participants are required to complete both phases. The estimated total involvement in the project is up to 20-24 hours.

Phase 1: Time allowed: 2 weeks

- Review 500 chest X-rays and express a clinical opinion through a structured reporting template (multiple choice, no open text required).

Rest/washout period of 2 weeks.

Phase 2 - Time allowed: 2 weeks

- Review 500 chest X-rays together with an AI report for each case and express your clinical opinion through the same structured reporting template used in Phase A.

Cases reading

Two consultant thoracic radiologists will independently review the images to establish the 'ground truth' findings on the CXRs, where a consensus is reached this will then be used as the reference standard. In the case of disagreement, a third senior thoracic radiologist's opinion (\>20 years experience) will undertake arbitration. A difficulty score will be assigned to each abnormality by the ground truthers using a 4-point Likert scale (1 being easy/obvious to 4 being hard/poorly visualised).

Ground truthing

This study has been added as a sub study to the Simulation Training for Emergency Department Imaging 2 study (ClinicalTrials.gov ID NCT05427838).

The Lunit INSIGHT CXR is a validation study that aims to assess the utility of an Artificial Intelligence-based (AI) chest X-ray (CXR) interpretation tool in assisting the diagnostic accuracy, speed, and confidence of a varied group of healthcare professionals. The study will be conducted using 500 retrospectively collected inpatient and emergency department CXRs from two United Kingdom (UK) hospital trusts. Two fellowship trained thoracic radiologists will independently review all studies to establish the ground truth reference standard. The Lunit INSIGHT CXR tool will be used to analyze each CXR, and its performance will be measured against the expert readers. The study will evaluate the utility of the algorithm in improving reader accuracy and confidence as measured by sensitivity, specificity, positive predictive value, and negative predictive value. The study will measure the performance of the algorithm against ten abnormal findings, including pulmonary nodules/mass, consolidation, pneumothorax, atelectasis, calcification, cardiomegaly, fibrosis, mediastinal widening, pleural effusion, and pneumoperitoneum. The study will involve readers from various clinical professional groups with and without the assistance of Lunit INSIGHT CXR. The study will provide evidence on the impact of AI algorithms in assisting healthcare professionals such as emergency medicine and general medicine physicians who regularly review images in their daily practice.

AI Assisted Detection of Chest X-Rays

Utility of an AI-based CXR Interpretation Tool in Assisting Diagnostic Accuracy, Speed, and Confidence of Healthcare Professionals: a Study Using 500 Retrospectively Collected Inpatient and Emergency Department CXRs From Two UK Hospital Trusts

Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine sensitivity.

Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard in order to determine specificity.

Evaluation of the Lunit INSIGHT CXR algorithm will be performed comparing it to the reference standard. Continuous probability score from the algorithm will be utilized for the ROC analyses, while binary classification results with a predefined operating cut-off will be used for evaluation of sensitivity, specificity, positive predictive value, and negative predictive value.

The study will include two sessions (with and without AI overlay), with all 30 readers reviewing all 500 CXR cases each time separated by a washout period to mitigate recall bias. The cases will be randomised between the two reads and for every reader.

Mean time taken to review a scan, with vs without AI assistance.

Oxford University Hospitals NHS Trust

Primary Investigator

Stomach Neoplasms

Syndrome

Liver Diseases

Respiratory Distress Syndrome

Respiratory Distress Syndrome, Newborn

Acute Lung Injury

Pancreatic Diseases

Airway Obstruction

Gastrointestinal Neoplasms

Digestive System Neoplasms

Stomach Diseases

Stomach Cancer

Respiratory Distress Syndrome, Infant

Acute Respiratory Distress Syndrome

Hematinics

Liver Extracts

Marc Bodenstein

The purpose of this study is to determine whether Electrical Impedance Tomography can be used as sensitive and specific predictor in the detection of postoperative pulmonary complications (e.g. pneumonia, bronchitis, acute respiratory distress syndrome, pleural effusion, pneumothorax, pulmonary edema, atelectasis, pulmonary embolism, hypoxemia, hypercapnia, spasms and obstructions of the airway) in patients undergoing epigastric surgery.

* Study cycle:

  1. Check of criteria of inclusion/exclusion
  2. Informed consent
  3. Recording preoperative data via paper case report form
  4. Preoperative EIT measurement
  5. Postoperative EIT measurement
  6. Recording postoperative data via paper case report form

     Patients therapy and any diagnostic investigation during hospital stay (apart from EIT measurement) are performed appropriate to patients individual clinical needs and are independent from participation in this clinical trial. As PPC may result from surgical complications (e.g. intra- /postoperative bleeding, anastomotic leak, infection of the wound, injury of neighboring organs) they are monitored.
* Statistical analysis:

Sample size:

After consultation of Johannes-Gutenberg-University Institute of Medical Biometry and Epidemiology the following procedure was developed: Pre-analysis with 20 patients, then determination of definitive sample size (advised 80 patients).

Population:

Any registered patient is included in population even if no study-treatment was performed. To fulfill Pre-Protocol-Population-criteria EIT-measurement has to be performed before and after elective epigastric surgery and information about PPC must be revealable from patients medical file/records.

* Analysis of efficacy:

  1. Primary objective:

     (A) Incidence of any PPC (postoperative pulmonary complication) until postoperative day 7; Acquisition of PPC might require diagnostic investigation during hospital stay; investigations are only performed appropriate to patients individual clinical needs. As PPC are defined:
     * I: desaturation, increased oxygen demand (data from electronic health record)
     * II: dys-/atelectasis, pleural effusion, pneumothorax, pulmonary vein congestion (data from thoracic x-ray)
     * III: detected respiratory infection/pneumonia, pulmonary edema, Aspiration, non-invasive/CPAP-Ventilation, necessity of pleural drain/of bronchoscopy (data from electronic health record, x-ray)
     * IV: Reintubation (data from electronic health record), ARDS (according to Berlin Definition)

     (B) regional Distribution of Ventilation (Analysis of EIT raw data pre- and postoperative in 4 regions of interest: ventral, mid-ventral, mid-dorsal, dorsal; correlation between PPC and difference in regional Distribution of Ventilation at defined moment)
  2. Secondary objective: post-hoc Analysis (see down Secondary Outcome Measure)

     Average, Median, Quartile, Standard Deviation are detected and shown in Box-plots (type of graphic). Explorative comparison of parameters is effected by t-Test, Mann-Whitney-U-Test, Fisher-Test dependent on normal distribution.
* Quality assurance and data management Design, execution and analyzation of the study is ICH-GCP-conform (Good-Clinical-Practice-Guideline of the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use). All responsible persons are obliged to good clinical practice. All changes in study protocol are to be authorized by responsible Ethics Committee. The P.I. is responsible for source data verification to assess the accuracy, completeness, or representativeness of registry data by comparing the data to external data sources (e.g., medical records, paper or electronic case report forms).

Prior to written informed consent all patients are to be informed about character and consequences of the study. With their signature patients permit collection and use of source data. Every patient receives an Identification code for pseudonymization of data. Analyzation and publication of data is effected anonymously only. Data collection is mainly provided in an electronic data base, besides in a paper-based Case Report Form. All electronic and paper-based data and archived in our Clinical Research Center and are accessible for authorized persons (Monitor, Auditor). The study protocol does not provide audits.

Amendment after Pre-analysis:

We conducted a preliminary study of 23 patients for sample size calculation, aligned with the goal of predicting probability of relevant PPC. Power calculation was based on the assumption that 50 % of patients would develop relevant PPC. We performed a power analysis using the Wilcoxon test. The assumption was that AUC of EIT-parameters for distinguishing PPC-affected patients from non-affected patients would be at least 0.65. In order to demonstrate that AUC is indeed above 0.5, a Wilcoxon test can be applied. AUC=0.65 translates into P(X\<Y)=0.65. To demonstrate this at the 5%-significance level with a power of 80%, 116 patients are needed. Considering that approximately 10% of patients may not be evaluable, a total sample size of 130 patients was determined. Interim analysis revealed, that incidence of relevant PPC in the study cohort was lower than assumed in sample size calculation. Therefore a higher sample size was necessary than expected. Furthermore, recruiting was slow. We therefore switched from a confirmatory to a descriptive approach.

Screening of Postoperative Pulmonary Complications by Electrical Impedance Tomography

Screening of Postoperative Pulmonary Complications by Electrical Impedance Tomography in Intensive Care Patients Undergoing Epigastric Surgery

Comparison of the changed regional ventilation partition to the occurence of postoperative pulmonary complications =\> calculation of sensitivity and specifity of the primary outcome measure to predict PPC. Statistical analysis: receiver operating characteristic (ROC curve).

Sensitivity and specifiy for appearance of PPC (Postoperative Pulmonary Complications) within Postoperative Day 1-7.

Statistical analysis: Sensitivity, Specificity, ROC-curve

Correlation of pre-existing pathologic pulmonary conditions with specific postoperative changes in regional distribution of lung ventilation and perfusion assessed by EIT Statistical analysis: Sensitivity, Specificity, ROC-curve

Appearance of all different forms of PPC within Postoperative Day 1-7 and relation to postoperative EIT-measure (ROI analysis in 8 pulmonary ROIs).

Statistical analysis: Sensitivity, Specificity, ROC-curve

Sensitivity and specifity of postoperative EIT-measure for mortality. Statistical analysis: Sensitivity, Specificity, ROC-curve

Sensitivity and specifity of postoperative EIT-measure for time of intensive care therapy.

Statistical analysis: Sensitivity, Specificity, ROC-curve

Sensitivity and specifity of postoperative EIT-measure for Ventilator-obligatory-free days.

Statistical analysis: Sensitivity, Specificity, ROC-curve

Johannes Gutenberg University Mainz

Dr. med.

Pulmonary Emphysema

Emphysema

Subcutaneous Emphysema

A total of 1,073 chest X-rays were acquired within the specified period at the department. The data collection remained intact and unaffected throughout the testing phase, ensuring the integrity of the dataset. The collected sample accurately represents the prevalence of findings within the observed population. After excluding ineligible studies such as X-rays from patients under 18 years of age, lateral projection X-rays, and scans of insufficient quality, a total of 956 relevant CXRs were identified for further assessment.

The proposed DLAD (Carebot AI CXR) is a deep learning-based medical device designed to assist radiologists in interpreting chest X-ray images acquired in anteroposterior (AP) or posteroanterior (PA) projection. By employing advanced deep learning algorithms, this solution enables automatic detection of abnormal findings by analyzing visual patterns associated with specific conditions. The targeted abnormalities include atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO). The DLAD functions as a prediction algorithm complemented by various application peripherals, such as web-based communication tools, DICOM file conversion capabilities, and storage and reporting libraries supporting both DICOM Structured Report and DICOM Presentation State formats.

The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists.

In the period between October 18th, 2022, and November 17th, 2022, anonymized chest X-ray images of patients were collected at the Radiodiagnostic Department of the Havířov Hospital, p.o. The collection process involved utilizing the CloudPACS imaging and archiving system provided by OR-CZ spol. s r.o.

The collected X-ray images were subjected to the proposed DLAD (Carebot AI CXR) for analysis. Subsequently, the DLAD's performance was compared with the standard clinical practice, where radiologists assessed the CXR images in the simulated hospital setting with access to standard viewing tools (e.g., pan, zoom, WW/WL) and were given an unlimited amount of time to complete the review. Each radiologist determined the presence or absence of 7 indicated radiological findings, including atelectasis (ATE), consolidation (CON), pleural effusion (EFF), pulmonary lesion (LES), subcutaneous emphysema (SCE), cardiomegaly (CMG), and pneumothorax (PNO).

Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice

The primary objective is to evaluate the performance parameters of the proposed DLAD (Carebot AI CXR) in comparison to individual radiologists. The performance test includes sensitivity and specificity, positive and negative likelihood ratio, and positive and negative predictive value. The aforementioned parameters are statistically compared using confidence intervals (CI) and p-Values. The comparison procedure consists of two steps: a global hypothesis test is conducted to determine whether there are significant differences between DLAD and radiologists. If the global hypothesis test yields a significant result, individual hypothesis tests are performed. Additionally, multiple comparison methods, such as McNemar with continuity correction for Se and Sp, Holm method for LRs, and weighted generalized score statistics for PVs, are applied to control the overall error rate. All tests are performed as two-tailed tests at the 5% significance level.

Liver Neoplasms

Central Nervous System Depressants

Anesthetics

Computed tomography scans are performed every 15 minute during the first 45 minutes during general anaesthesia using high frequency jet ventilation.

On High frequency jet ventilation, Every 15 minutes, after induction of general anaesthesia, a computed tomography of the lower part of the lungs are taken (10 cm volume, with the top of the right diaphragm as the lower border)

HFJV

Atelectasis formation using HFJV during stereotactic solid organ ablations.

Primary endpoint will be to study the formation of atelectasis during HFJV. Secondary endpoint will be to study liver displacement over time. Reference groups will be found in previous published articles as referred to above.

Materials and methods: A radiological protocol has been made for the CT-scanner to take 10 cm volume Scans of the lower part of the lung. The scans will have its' lower border tangential to the top of the diaphragm. The first scan will be performed in the same session as the routine scan of the liver is performed after the patient is anesthetized. These Scans will then be repeated every 15 minute during the first 45 minutes. Arterial bloodgas analysis, transcutaneous CO2, blood pressure, saturation as well as parameters from the jet ventilator and the standard ventilator will be recorded.

The use of HFJV in abdominal surgery as well as in out-of-operating theatre environment, is not well studied. In percutaneous liver thermal ablation the patient is anesthetized and intubated on the CT-bed where the liver ablation procedure is performed. Atelectasis is formed as soon as the patient is supine. Anesthesia itself also contributes to this. In extreme cases almost half the lung can be collapsed during anaesthesia, before any surgery has taken place.

The investigators have noticed that during this specific surgery, dynamic lung compliance does not necessarily worsen during HFJV but rather being unchanged or in some cases even becoming better and want to study this further and investigate the formation of atelectasis during HFJV. There are studies where atelectasis has been measured in a similar way as in this planned study, that is, with thin axial CT-scans at representative levels of the lung. This is during conventional lung ventilation, and has not yet been studied during HFJV.

Inclusion criteria: A total of n= 25 patients planned for elective liver tumor thermal ablation will be recruited after written informed consent.

Exclusion criteria: Patients 1/ under the age of 50 years, 2/with severe, poorly controlled lung disease.

The lung images will be saved in DICOM format and the tissue density will be analysed quantitatively. For each scan, the inner contour of each hemithorax will be manually drawn, excluding the chest wall, mediastinum, pleural effusions, and regions representing partial volume effects. Liver displacement can be measured from the routine CT-scans taken before and after the procedure.

Statistical methods: Chi2-analysis will be used to calculate the percentage of atelectasis in the lung.

Power calculation: Since the rate of atelectasis formation in the study groups is not known, it is impossible to perform power calculation for this trial. This study may clarify this issue in the planning of further studies.

This study may contribute to more knowledge about HFJV and its' effect on lung physiology, it may also contribute as a generator of new hypothesis and it might be a follow up study where the effect of alveolar recruitment maneuver (ARM) is studied.

Atelectasis Formation Using HFJV During Stereotactic Solid Organ Ablations

Atelectasis Formation Using High Frequency Jet Ventilation During Stereotactic Solid Organ Ablations

Computed tomography Scans will be taken during general anaesthesia at the start of jet ventilation, t=0, and then at t=15 minutes, t=30 minutes and t=45 minutes. A total number of four scans will be performed in each study subject.

Routine CT Scans taken during surgery will be studied to calculate liver displacement over time.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Retrospective Study of Carebot AI CXR Performance in Preclinical Practice	NCT05594485	Artificial Inte... Lung Diseases Pneumothorax Pleural Effusio... Cardiomegaly Lung Cancer Pulmonary Edema Consolidation Pneumonia Atelectasis Hilar Calcifica... Fracture Rib	Carebot AI CXR	18 Years -	Carebot s.r.o.
AI Assisted Detection of Chest X-Rays	NCT06075836	Pulmonary Nodul... Pulmonary Nodul... Pulmonary Conso... Pneumothorax Pneumothorax; A... Atelectasis Pulmonary Calci... Cardiomegaly Fibrosis Lung Pleural Effusio... Pleural Effusio... Pneumoperitoneu...	Cases reading Ground truthing	-	Oxford University Hospitals NHS Trust
Screening of Postoperative Pulmonary Complications by Electrical Impedance Tomography	NCT02337673	Pulmonary Disea... Postoperative R... Malignant Neopl... Liver Diseases Pancreatic Dise...		18 Years -	Johannes Gutenberg University Mainz
AI Assisted Detection of Chest X-Rays	NCT06075836	Pulmonary Nodul... Pulmonary Nodul... Pulmonary Conso... Pneumothorax Pneumothorax; A... Atelectasis Pulmonary Calci... Cardiomegaly Fibrosis Lung Pleural Effusio... Pleural Effusio... Pneumoperitoneu...	Cases reading Ground truthing	-	Oxford University Hospitals NHS Trust
Multi-Reader Retrospective Study Examining Carebot AI CXR 2.0.21-v2.01 Implementation in Everyday Radiology Clinical Practice	NCT05963945	Pneumothorax Pulmonary Nodul... Atelectasis Subcutaneous Em... Cardiomegaly Consolidation Pleural Effusio...	Carebot AI CXR	18 Years -	Carebot s.r.o.
Atelectasis Formation Using HFJV During Stereotactic Solid Organ Ablations	NCT03378752	High Frequency ... Atelectasis	HFJV	50 Years -	Karolinska Institutet

Spots Global Cancer Trial Database for atelectasis

Every month we try and update this database with for atelectasis cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Spots Global Cancer Trial Database for atelectasis

Every month we try and update this database with for atelectasis cancer trials from around the world to help patients and their families find trials that might be right for them. We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.

Every month we try and update this database with for atelectasis cancer trials from around the world to help patients and their families find trials that might be right for them.
We offer this 100% free of charge and do not endorse any of the trials listed here, we hope it helps you or a loved one.