Computer-aided Detection Software Research Articles

A quantitative analysis of progressive fibrosing interstitial lung disease on computed tomography for the assessment of decreased vital capacity.

The results of a quantitative analysis of computed tomography (CT) of interstitial lung disease (ILD) using a computer-aided detection (CAD) technique were correlated with the results of pulmonary function tests. To evaluate the correlation between a quantitative analysis of CT of progressive fibrosing interstitial lung disease (PF-ILD) including idiopathic pulmonary fibrosis (IPF) and non-IPF, which can manifest progressive pulmonary fibrosis and the vital capacity (VC), and to identify indicators for the assessment of a decreased VC. A total of 73 patients (46 patients with IPF and 27 patients with non-IPF) were included in this study. Associations between the quantitative analysis of CT and the %VC using a CAD software program were investigated using Spearman's rank correlation and a logistic regression analysis. The appropriate cutoff vale for predicting a decreased VC was determined (%VC <80) and the area under the curve (AUC) was calculated. A multiple logistic regression analysis showed that the total extent of interstitial pneumonia on CT was a significant indicator of a decreased VC (P = 0.0001; odds ratio [OR]=1.15; 95% confidence interval [CI]=1.06-1.27 in IPF and P = 0.0025; OR=1.16; 95% CI=1.03-1.30 in non-IPF). The cutoff values of the total extent of interstitial pneumonia in IPF and non-IPF for predicting a decreased VC were determined to be 23.3% and 21.5%, and the AUCs were 0.83 and 0.91, respectively. A quantitative analysis of CT of PF-ILD using a CAD software program could be useful for predicting a decreased VC.

Performance changes due to differences among annotating radiologists for training data in computerized lesion detection.

The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.

Evaluation of a population-wide, systematic screening initiative for tuberculosis on Daru island, Western Province, Papua New Guinea

BackgroundA population-wide, systematic screening initiative for tuberculosis (TB) was implemented on Daru island in the Western Province of Papua New Guinea, where TB is known to be highly prevalent. The initiative used a mobile van equipped with a digital X-ray device, computer-aided detection (CAD) software to identify TB-related abnormalities on chest radiographs, and GeneXpert machines for follow-on diagnostic testing. We describe the results of the TB screening initiative, evaluate its population-level impact and examine risk factors associated with TB detection.MethodsThrough a retrospective review of screening data, we assessed the effectiveness of the screening by examining the enrolment coverage and the proportion of people with TB among screened subjects. A cascade analysis was performed to illustrate the flow of participants in the screening algorithm. We conducted univariate and multivariate analyses to identify factors associated with TB. Furthermore, we estimated the number of additional cases detected by the project by examining the trend of routine TB case notifications during the intervention period, compared to the historical baseline cases and trend-adjusted expected cases.ResultsOf the island’s 18,854 residents, 8,085 (42.9%) were enrolled and 7,970 (98.6%) had chest X-ray interpreted by the CAD4TB software. A total of 1,116 (14.0%) participants were considered to have abnormal CXR. A total of 69 Xpert-positive cases were diagnosed, resulting in a detection rate of 853 per 100 000 population screened. 19.4% of people with TB had resistance to rifampicin. People who were in older age groups (aOR 6.6, 95%CI: 1.5–29.1 for the 45–59 age group), were severely underweight (aOR 2.5, 95%CI:1.0-6.1) or underweight (aOR 2.1, 95%CI: 1.1–3.8), lived in households < 5 people (aOR 3.4, 95%CI:1.8–6.6) and had a past history of TB (aOR 2.1, 95%CI: 1.2–3.6) were more likely to have TB. The number of bacteriologically confirmed TB notified during the intervention period was 79.3% and 90.8% higher than baseline notifications and forecasted notifications, respectively.ConclusionThe screening project demonstrated its effectiveness with the high Xpert-positive TB prevalence among the participants and by successfully yielding additional cases of bacteriologically confirmed TB including rifampicin-resistant TB. The results and lessons learnt from the project should inform future TB screening initiatives in Papua New Guinea.

National cross-sectional cluster survey of tuberculosis prevalence in Timor-Leste: a study protocol

IntroductionTimor-Leste has one of the world’s highest estimated tuberculosis (TB) incidences, yet the data which informs this estimate is limited and the true burden of TB disease is not known....

Accuracy of digital chest x-ray analysis with artificial intelligence software as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru.

Tuberculosis (TB) transmission in healthcare facilities is common in high-incidence countries. Yet, the optimal approach for identifying inpatients who may have TB is unclear. We evaluated the diagnostic accuracy of qXR (Qure.ai, India) computer-aided detection (CAD) software versions 3.0 and 4.0 (v3 and v4) as a triage and screening tool within the FAST (Find cases Actively, Separate safely, and Treat effectively) transmission control strategy. We prospectively enrolled two cohorts of patients admitted to a tertiary hospital in Lima, Peru: one group had cough or TB risk factors (triage) and the other did not report cough or TB risk factors (screening). We evaluated the sensitivity and specificity of qXR for the diagnosis of pulmonary TB using culture and Xpert as primary and secondary reference standards, including stratified analyses based on risk factors. In the triage cohort (n = 387), qXR v4 sensitivity was 0.91 (59/65, 95% CI 0.81-0.97) and specificity was 0.32 (103/322, 95% CI 0.27-0.37) using culture as reference standard. There was no difference in the area under the receiver-operating-characteristic curve (AUC) between qXR v3 and qXR v4 with either a culture or Xpert reference standard. In the screening cohort (n = 191), only one patient had a positive Xpert result, but specificity in this cohort was high (>90%). A high prevalence of radiographic lung abnormalities, most notably opacities (81%), consolidation (62%), or nodules (58%), was detected by qXR on digital CXR images from the triage cohort. qXR had high sensitivity but low specificity as a triage in hospitalized patients with cough or TB risk factors. Screening patients without cough or risk factors in this setting had a low diagnostic yield. These findings further support the need for population and setting-specific thresholds for CAD programs.

Breast density in NF1 women: a retrospective study

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition caused by neurofibromin haploinsufficiency due to pathogenic variants in the NF1 gene. Tumor predisposition has long been associated with NF1, and an increased breast cancer (BC) incidence and reduced survival have been reported in recent years for women with NF1. As breast density is another known independent risk factor for BC, this study aims to evaluate the variability of breast density in patients with NF1 compared to the general population. Mammograms from 98 NF1 women affected by NF1, and enrolled onto our monocentric BC screening program, were compared with those from 300 healthy subjects to verify differences in breast density. Mammograms were independently reviewed and scored by a radiologist and using a Computer-Aided Detection (CAD) software. The comparison of breast density between NF1 patients and controls was performed through Chi-squared test and with multivariable ordinal logistic models adjusted for age, body mass index (BMI), number of pregnancies, and menopausal status.breast density was influenced by BMI and menopausal status in both NF1 patients and healthy subjects. No difference in breast density was observed between NF1 patients and the healthy female population, even after considering the potential confounding factors.Although NF1 and a highly fibroglandular breast are known risk factors of BC, in this study, NF1 patients were shown to have comparable breast density to healthy subjects. The presence of pathogenic variants in the NF1 gene does not influence the breast density value.

Computer-aided detection thresholds for digital chest radiography interpretation in tuberculosis diagnostic algorithms.

Use of computer-aided detection (CAD) software is recommended to improve tuberculosis screening and triage, but threshold determination is challenging if reference testing has not been performed in all individuals. We aimed to determine such thresholds through secondary analysis of the 2019 Lesotho national tuberculosis prevalence survey. Symptom screening and chest radiographs were performed in participants aged ≥15 years; those symptomatic or with abnormal chest radiographs provided samples for Xpert MTB/RIF and culture testing. Chest radiographs were processed using CAD4TB version 7. We used six methodological approaches to deal with participants who did not have bacteriological test results to estimate pulmonary tuberculosis prevalence and assess diagnostic accuracy. Among 17 070 participants, 5214 (31%) had their tuberculosis status determined; 142 had tuberculosis. Prevalence estimates varied between methodological approaches (0.83-2.72%). Using multiple imputation to estimate tuberculosis status for those eligible but not tested, and assuming those not eligible for testing were negative, a CAD4TBv7 threshold of 13 had a sensitivity of 89.7% (95% CI 84.6-94.8) and a specificity of 74.2% (73.6-74.9), close to World Health Organization (WHO) target product profile criteria. Assuming all those not tested were negative produced similar results. This is the first study to evaluate CAD4TB in a community screening context employing a range of approaches to account for unknown tuberculosis status. The assumption that those not tested are negative - regardless of testing eligibility status - was robust. As threshold determination must be context specific, our analytically straightforward approach should be adopted to leverage prevalence surveys for CAD threshold determination in other settings with a comparable proportion of eligible but not tested participants.

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.

Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as 'abnormal' or 'normal'. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

Computer-Aided Detection for Chest Radiography to Improve the Quality of Tuberculosis Diagnosis in Vietnam's District Health Facilities: An Implementation Study.

In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing.

Evaluation of A Computer-Aided Detection Software for Prostate Cancer Prediction: Excellent Diagnostic Accuracy Independent of Preanalytical Factors

Prostate cancer (PCa) is the most common noncutaneous cancer in men in the Western world. In addition to accurate diagnosis, Gleason grading and tumor volume estimates are critical for patient management. Computer-aided detection (CADe) software can be used to facilitate the diagnosis and improve the diagnostic accuracy and reporting consistency. However, preanalytical factors such as fixation and staining of prostate biopsy specimens and whole slide images (WSI) on scanners can vary significantly between pathology laboratories and may, therefore, impact the quality of WSI and utility of CADe algorithms. We evaluated the performance of a CADe software in predicting PCa on WSIs of prostate biopsy specimens and focused on whether there were any significant differences in image quality between WSIs obtained on different scanners and specimens from different histopathology laboratories. Thirty prostate biopsy specimens from 2 histopathology laboratories in the United States were included in this study. The hematoxylin and eosin slides of the biopsy specimens were scanned on 3 scanners, generating 90 WSIs. These WSIs were then analyzed using a CADe software (INIFY Prostate, Algorithm), which identified and annotated all areas suspicious for PCa and calculated the tumor volume (percentage area of the biopsy core involved). Study pathologists then reviewed the Algorithm’s annotations and tumor volume calculation to confirm the diagnosis and identify benign glands that were misclassified as cancer (false positive) and cancer glands that were misclassified as benign (false negative). The CADe software worked equally well on WSIs from all 3 scanners and from both laboratories, with similar sensitivity and specificity. The overall sensitivity was 99.4%, and specificity was 97%. The percentage of suspicious cancer areas calculated by the Algorithm was similar for all 3 scanners. For WSIs with small foci of cancer (<1 mm), the Algorithm identified all cancer glands (sensitivity, 100%). Preanalytical factors had no significant impact on whole slide imaging and subsequent application of a CADe software. Prediction accuracy could potentially be further improved by processing biopsy specimens in a centralized histology laboratory and training the Algorithm on WSIs from the same laboratory in order to minimize variations in preanalytical factors and optimize the diagnostic performance of the Algorithm.

Diagnostic Performance of Artificial Intelligence–Based Computer-Aided Detection Software for Automated Breast Ultrasound

This study aimed to evaluate the diagnostic performance of radiologists following the utilization of artificial intelligence (AI)-based computer-aided detection software (CAD) in detecting suspicious lesions in automated breast ultrasounds (ABUS). ABUS-detected 262 breast lesions (histopathological verification; January 2020 to December 2022) were included. Two radiologists reviewed the images and assigned a Breast Imaging Reporting and Data System (BI-RADS) category. ABUS images were classified as positive or negative using AI-CAD. The BI-RADS category was readjusted in four ways: the radiologists modified the BI-RADS category using the AI results (AI-aided 1), upgraded or downgraded based on AI results (AI-aided 2), only upgraded for positive results (AI-aided 3), or only downgraded for negative results (AI-aided 4). The AI-aided diagnostic performances were compared to radiologists. The AI-CAD-positive and AI-CAD-negative cancer characteristics were compared. For 262 lesions (145 malignant and 117 benign) in 231 women (mean age, 52.2 years), the area under the receiver operator characteristic curve (AUC) of radiologists was 0.870 (95% confidence interval [CI], 0.832-0.908). The AUC significantly improved to 0.919 (95% CI, 0.890-0.947; P=0.001) using AI-aided 1, whereas it improved without significance to 0.884 (95% CI, 0.844-0.923), 0.890 (95% CI, 0.852-0.929), and 0.890 (95% CI, 0.853-0.928) using AI-aided 2, 3, and 4, respectively. AI-CAD-negative cancers were smaller, less frequently exhibited retraction phenomenon,and had lower BI-RADS category. Among nonmass lesions, AI-CAD-negative cancers showed no posterior shadowing. AI-CAD implementation significantly improved the radiologists' diagnostic performance and may serve as a valuable diagnostic tool.

The radiology quality assurance process in the Yorkshire Lung Screening Trial, and findings from the baseline round of low dose CT screening for lung cancer.

As lung cancer screening is rolled-out, there is a need to develop an effective quality assurance (QA) framework around radiology reporting to ensure optimal implementation. Here, we report a structured QA process for low-dose CT (LDCT) scans performed in the Yorkshire Lung Screening Trial. Negative LDCT scans were single read after using computer-aided detection software. The radiology QA process included reviewing 5% of negative scans selected at random, and all cases with a subsequent diagnosis of extrapulmonary cancer or interval lung cancer not detected on the baseline scan. Radiologists were not informed of the reason for review and original radiology reports were scored as either "satisfactory", "satisfactory with learning points", or "unsatisfactory". From 6650 participants undergoing LDCT screening, 208 negative scans were reviewed alongside 11 cases with subsequent extrapulmonary cancer and 10 cases with interval lung cancer. Overall, only three reports were ultimately judged "unsatisfactory", 1% of randomly selected negative scans (n = 2/208) and one interval lung cancer scan (n = 1/10). Four out of a total of five cases judged "satisfactory with learning points" were related to oesophageal abnormalities where the participant was subsequently diagnosed with oesophageal cancer. The described process attempts to minimise bias in retrospective review of screening scans, and may represent a framework for future QA of national screening programmes. This study describes a structured QA process for a lung cancer screening programme, involving blinded second-read of LDCT screening scans to ensure fair, constructive audit of clinical performance.

Incidental radiological findings during clinical tuberculosis screening in Lesotho and South Africa: a case series

BackgroundChest X-ray offers high sensitivity and acceptable specificity as a tuberculosis screening tool, but in areas with a high burden of tuberculosis, there is often a lack of radiological expertise to interpret chest X-ray. Computer-aided detection systems based on artificial intelligence are therefore increasingly used to screen for tuberculosis-related abnormalities on digital chest radiographies. The CAD4TB software has previously been shown to demonstrate high sensitivity for chest X-ray tuberculosis-related abnormalities, but it is not yet calibrated for the detection of non-tuberculosis abnormalities. When screening for tuberculosis, users of computer-aided detection need to be aware that other chest pathologies are likely to be as prevalent as, or more prevalent than, active tuberculosis. However, non­-tuberculosis chest X-ray abnormalities detected during chest X-ray screening for tuberculosis remain poorly characterized in the sub-Saharan African setting, with only minimal literature.Case presentationIn this case series, we report on four cases with non-tuberculosis abnormalities detected on CXR in TB TRIAGE + ACCURACY (ClinicalTrials.gov Identifier: NCT04666311), a study in adult presumptive tuberculosis cases at health facilities in Lesotho and South Africa to determine the diagnostic accuracy of two potential tuberculosis triage tests: computer-aided detection (CAD4TB v7, Delft, the Netherlands) and C-reactive protein (Alere Afinion, USA). The four Black African participants presented with the following chest X-ray abnormalities: a 59-year-old woman with pulmonary arteriovenous malformation, a 28-year-old man with pneumothorax, a 20-year-old man with massive bronchiectasis, and a 47-year-old woman with aspergilloma.ConclusionsSolely using chest X-ray computer-aided detection systems based on artificial intelligence as a tuberculosis screening strategy in sub-Saharan Africa comes with benefits, but also risks. Due to the limitation of CAD4TB for non-tuberculosis-abnormality identification, the computer-aided detection software may miss significant chest X-ray abnormalities that require treatment, as exemplified in our four cases. Increased data collection, characterization of non-tuberculosis anomalies and research on the implications of these diseases for individuals and health systems in sub-Saharan Africa is needed to help improve existing artificial intelligence software programs and their use in countries with high tuberculosis burden.

A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm.

Chest radiography (chest X-ray or CXR) plays an important role in the early detection of active pulmonary tuberculosis (TB). In areas with a high TB burden that require urgent screening, there is often a shortage of radiologists available to interpret the X-ray results. Computer-aided detection (CAD) software employed with artificial intelligence (AI) systems may have the potential to solve this problem. We validated the effectiveness and safety of pulmonary tuberculosis imaging screening software that is based on a convolutional neural network algorithm. We conducted prospective multicenter clinical research to validate the performance of pulmonary tuberculosis imaging screening software (JF CXR-1). Volunteers under the age of 15 years, both with or without suspicion of pulmonary tuberculosis, were recruited for CXR photography. The software reported a probability score of TB for each participant. The results were compared with those reported by radiologists. We measured sensitivity, specificity, consistency rate, and the area under the receiver operating characteristic curves (AUC) for the diagnosis of tuberculosis. Besides, adverse events (AE) and severe adverse events (SAE) were also evaluated. The clinical research was conducted in six general infectious disease hospitals across China. A total of 1,165 participants were enrolled, and 1,161 were enrolled in the full analysis set (FAS). Men accounted for 60.0% (697/1,161). Compared to the results from radiologists on the board, the software showed a sensitivity of 94.2% (95% CI: 92.0-95.8%) and a specificity of 91.2% (95% CI: 88.5-93.2%). The consistency rate was 92.7% (91.1-94.1%), with a Kappa value of 0.854 (P = 0.000). The AUC was 0.98. In the safety set (SS), which consisted of 1,161 participants, 0.3% (3/1,161) had AEs that were not related to the software, and no severe AEs were observed. The software for tuberculosis screening based on a convolutional neural network algorithm is effective and safe. It is a potential candidate for solving tuberculosis screening problems in areas lacking radiologists with a high TB burden.

Computer-Aided Design on Image Detection: A Chronology Review

In order to conduct research and diagnose diseases, image processing is essential. Medical professionals frequently segment images for pre- and post-surgery decisions, which are necessary for treatment planning. In the medical research field, segmentation is the core subject of several studies. Computer-aided detection (CAD) is utilised to achieve the highest level of classification accuracy and may be used to identify tissues growing abnormally. For the purpose of finding abnormalities, magnetic resonance imaging (MRI) is an effective approach, but it takes time and requires a fair amount of human resources. This approach, however, was problematic for slicing data related to the interior surfaces of cavity structures, for instance, the human skull. As a result, a ray casting algorithm was used to create a software programme. The most significant problem with segmentation techniques for x-ray images is seed point selection. An object's surface structure is described by a three-dimensional (3D) surface structure graph (SSG) that was created during segmentation. Ultrasound image detection is critical today. The model can be further modified using this CAD software so that it can be reproduced on a rapid prototyping device in the STL file format. The suggested deep learning method is exceptionally effective in accurately detecting faults in each layer, according to experimental data.

Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges.

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database "AI for radiology" was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.

A quantitative analysis of long-term follow-up computed tomography of idiopathic pulmonary fibrosis: the correlation with the progression and prognosis.

Quantitative analyses of computed tomography (CT) images using computer-aided detection (CAD) are correlated with visual assessments and pulmonary function test findings and might be useful for predicting the prognosis of patients with idiopathic pulmonary fibrosis (IPF). To evaluate the association between the quantitative analysis of long-term follow-up CT of IPF and the progression and prognosis. A total of 48 patients with IPF who received over one year of follow-up CT were included in this study. The results of quantitative analyses (emphysema, ground-glass attenuation [GGA], consolidation, reticulation, and honeycombing) using a CAD software program of initial and follow-up CT findings were evaluated, and the association with the progression of the total lesion of IPF and prognosis using Spearman's rank correlation and Cox regression analyses was considered. Results of quantitative analyses of consolidation, reticulation, honeycombing, and the total lesion on initial CT were correlated with progressive changes in the total lesion of IPF per year (r = 0.4375, 0.4128, 0.4649, and 0.4095, respectively). The results of quantitative analyses of honeycombing (hazard ratio [HR] = 1.40, 95% confidence interval [CI] = 1.03-1.89, P = 0.0314) and GGA (HR = 0.85, 95% CI = 0.72-0.99, P = 0.0384) at initial CT were prognostic factors according to a multivariate Cox regression analysis. The quantitative analysis of honeycombing using a CAD software program of CT findings may be useful for predicting the progression and prognosis of patients with IPF.

The rise of artificial intelligence reading of chest X-rays for enhanced TB diagnosis and elimination.

We provide an overview of the latest evidence on computer-aided detection (CAD) software for automated interpretation of chest radiographs (CXRs) for TB detection. CAD is a useful tool that can assist in rapid and consistent CXR interpretation for TB. CAD can achieve high sensitivity TB detection among people seeking care with symptoms of TB and in population-based screening, has accuracy on-par with human readers. However, implementation challenges remain. Due to diagnostic heterogeneity between settings and sub-populations, users need to select threshold scores rather than use pre-specified ones, but some sites may lack the resources and data to do so. Efficient standardisation is further complicated by frequent updates and new CAD versions, which also challenges implementation and comparison. CAD has not been validated for TB diagnosis in children and its accuracy for identifying non-TB abnormalities remains to be evaluated. A number of economic and political issues also remain to be addressed through regulation for CAD to avoid furthering health inequities. Although CAD-based CXR analysis has proven remarkably accurate for TB detection in adults, the above issues need to be addressed to ensure that the technology meets the needs of high-burden settings and vulnerable sub-populations.

Artificial intelligence-reported chest X-ray findings of culture-confirmed pulmonary tuberculosis in people with and without diabetes

ObjectivesWe applied computer-aided detection (CAD) software for chest X-ray (CXR) analysis to determine if diabetes affects the radiographic presentation of tuberculosis. MethodsFrom March 2017-July 2018, we consecutively enrolled adults being evaluated for pulmonary tuberculosis in Karachi, Pakistan. Participants had same-day CXR, two sputum mycobacterial cultures, and random blood glucose measurement. We identified diabetes through self-report or glucose >11.1mMol/L. We included participants with culture-confirmed tuberculosis for this analysis. We used linear regression to estimate associations between CAD-reported tuberculosis abnormality score (range 0.00 to 1.00) and diabetes, adjusting for age, body mass index, sputum smear-status, and prior tuberculosis. We also compared radiographic abnormalities between participants with and without diabetes. Results63/272 (23%) of included participants had diabetes. After adjustment, diabetes was associated with higher CAD tuberculosis abnormality scores (p < 0.001). Diabetes was not associated with frequency of CAD-reported radiographic abnormalities apart from cavitary disease; participants with diabetes were more likely to have cavitary disease (74.6% vs 61.2% p = 0.07), particularly non-upper zone cavitary disease (17% vs 7.8%, p = 0.09). ConclusionsCAD analysis of CXR suggests diabetes is associated with more extensive radiographic abnormalities and with greater likelihood of cavities outside upper lung zones.

Early user experience and lessons learned using ultra-portable digital X-ray with computer-aided detection (DXR-CAD) products: A qualitative study from the perspective of healthcare providers.

Recent technological and radiological advances have renewed interest in using X-rays to screen and triage people with tuberculosis (TB). The miniaturization of digital X-ray (DXR), combined with automatic interpretation using computer-aided detection (CAD) software can extend the reach of DXR screening interventions for TB. This qualitative study assessed early implementers' experiences and lessons learned when using ultra-portable (UP) DXR systems integrated with CAD software to screen and triage TB. Semi-structured interviews were conducted with project staff and healthcare workers at six pilot sites. Transcripts were coded and analyzed using a framework approach. The themes that emerged were subsequently organized and presented using the Consolidated Framework for Implementation Research (CFIR). There were 26 interviewees with varying roles: supervisory, clinicians, radiographers, and radiologists. Participants recognized the portability as the main advantage, but criticize that it involves several compromises on throughput, internet dependence, manoeuvrability, and stability, as well as suitability for patients with larger body sizes. Furthermore, compared to using hardware and software from the same supplier and without digital health information systems, complexity increases with interoperability between hardware and software, and between different electronic health information systems. Currently, there is a limited capacity to implement these technologies, especially due to the need for threshold selection, and lack of guidance on radiation protection suitable for UP DXR machines. Finally, the respondents stressed the importance of having protected means of sharing patient medical data, as well as comprehensive support and warranty plans. Study findings suggest that UP DXR with CAD was overall well received to decentralize radiological assessment for TB, however, the improved portability involved programmatic compromises. The main barriers to uptake included insufficient capacity and lack of guidance on radiation protection suitable for UP DXR.