Detecting Mandible Fractures in CBCT Scans Using a 3-Stage Neural Network.

After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation.

Purpose: Few studies have evaluated real-world performance of radiological AI-tools in clinical practice. Over one-year, we prospectively evaluated the use of AI software to support the detection of intracranial large vessel occlusions (LVO) on CT angiography (CTA). Method: Quantitative measures (user log-in attempts, AI standalone performance) and qualitative data (user surveys) were reviewed by a key-user group at three timepoints. A total of 491 CTA studies of 460 patients were included for analysis. Results: The overall accuracy of the AI-tool for LVO detection and localization was 87.6%, sensitivity 69.1% and specificity 91.2%. Out of 81 LVOs, 31 of 34 (91%) M1 occlusions were detected correctly, 19 of 38 (50%) M2 occlusions, and 6 of 9 (67%) ICA occlusions. The product was considered user-friendly. The diagnostic confidence of the users for LVO detection remained the same over the year. The last measured net promotor score was -56%. The use of the AI-tool fluctuated over the year with a declining trend. Conclusions: Our pragmatic approach of evaluating the AI-tool used in clinical practice, helped us to monitor the usage, to estimate the perceived added value by the users of the AI-tool, and to make an informed decision about the continuation of the use of the AI-tool.

Automated detection of cerebral microbleeds via segmentation in susceptibility-weighted images of patients with traumatic brain injury.

Cerebral microbleeds (CMBs) are a recognised biomarker of traumatic axonal injury (TAI). Their number and location provide valuable information in the long-term prognosis of patients who sustained a traumatic brain injury (TBI). Accurate detection of CMBs is necessary for both research and clinical applications. CMBs appear as small hypointense lesions on susceptibility-weighted magnetic resonance imaging (SWI). Their size and shape vary markedly in cases of TBI. Manual annotation of CMBs is a difficult, error-prone, and time-consuming task. Several studies addressed the detection of CMBs in other neuropathologies with convolutional neural networks (CNNs). In this study, we developed and contrasted a classification (Patch-CNN) and two segmentation (Segmentation-CNN, U-Net) approaches for the detection of CMBs in TBI cases. The models were trained using 45 datasets, and the best models were chosen according to 16 validation sets. Finally, the models were evaluated on 10 TBI and healthy control cases, respectively. Our three models outperform the current status quo in the detection of traumatic CMBs, achieving higher sensitivity at low false positive (FP) counts. Furthermore, using a segmentation approach allows for better precision. The best model, the U-Net, achieves a detection rate of 90% at FP counts of 17.1 in TBI patients and 3.4 in healthy controls.

Image-based automated Psoriasis Area Severity Index scoring by Convolutional Neural Networks.

BACKGROUND: The Psoriasis Area and Severity Index (PASI) score is commonly used in clinical practice and research to monitor disease severity and determine treatment efficacy. Automating the PASI score with deep learning algorithms, like Convolutional Neural Networks (CNNs), could enable objective and efficient PASI scoring. OBJECTIVES: To assess the performance of image-based automated PASI scoring in anatomical regions by CNNs and compare the performance of CNNs to image-based scoring by physicians. METHODS: Imaging series were matched to PASI subscores determined in real life by the treating physician. CNNs were trained using standardized imaging series of 576 trunk, 614 arm and 541 leg regions. CNNs were separately trained for each PASI subscore (erythema, desquamation, induration and area) in each anatomical region (trunk, arms and legs). The head region was excluded for anonymity. Additionally, PASI-trained physicians retrospectively determined image-based subscores on the test set images of the trunk. Agreement with the real-life scores was determined with the intraclass correlation coefficient (ICC) and compared between the CNNs and physicians. RESULTS: Intraclass correlation coefficients between the CNN and real-life scores of the trunk region were 0.616, 0.580, 0.580 and 0.793 for erythema, desquamation, induration and area, respectively, with similar results for the arms and legs region. PASI-trained physicians (N = 5) were in moderate-good agreement (ICCs 0.706-0.793) with each other for image-based PASI scoring of the trunk region. ICCs between the CNN and real-life scores were slightly higher for erythema (0.616 vs. 0.558), induration (0.580 vs. 0.573) and area scoring (0.793 vs. 0.694) than image-based scoring by physicians. Physicians slightly outperformed the CNN on desquamation scoring (0.580 vs. 0.589). CONCLUSIONS: Convolutional Neural Networks have the potential to automatically and objectively perform image-based PASI scoring at an anatomical region level. For erythema, desquamation and induration scoring, CNNs performed similar to physicians, while for area scoring CNNs outperformed physicians on image-based PASI scoring.

Medial closing wedge high tibial osteotomy for valgus tibial deformities: good clinical results and survival with a mean 4.5 years of follow-up in 113 patients.

PURPOSE: A varus-producing medial closing wedge high tibial osteotomy (MCWHTO) is an uncommon procedure. The aim of this retrospective study was to assess the survivorship and prevalence of post-operative subjective knee laxity and satisfaction in a large cohort of patients with a MCWHTO performed without a MCL-reefing procedure. METHODS: All patients (n = 176) who underwent a MCWHTO in our clinic between 2008 and 2016 were approached to participate. After review of patient charts, questionnaires were sent to willingly patients. Primary outcome was the survivorship of the MCWHTO; secondary outcome was patient-reported instability and satisfaction. RESULTS: One-hundred and thirteen patients participated in the study. The 5-year survival rate of the MCWHTO was almost 80%. A total of 77% of the patients was satisfied with the treatment. With regard to post-operative subjective knee laxity, 26% of the patients experienced instability of the knee post-operation. Instability was significantly correlated with the KOOS domains, the Lysholm score, the IKDC knee function score and the Physical and Mental Health Domains of the SF-36. CONCLUSION: Medial closing wedge high tibial osteotomy provides good results regarding survivorship and patient satisfaction for patients with a valgus deformity which is located in the proximal tibia. Clinically relevant is that in the surgical technique without MCL-reefplasty instability is significantly correlated with worse patient-reported outcome measures. The addition of a MCL reefing procedure will improve outcome in selected patients. LEVEL OF EVIDENCE: III.

Automated chest X-ray reading for tuberculosis in the Philippines to improve case detection: a cohort study.

BACKGROUND: DetecTB (Diagnostic Enhanced Tools for Extra Cases of TB), an intensified tuberculosis (TB) case-finding programme targeting prisons and high-risk communities was implemented on Palawan Island, the Philippines.OBJECTIVE: To evaluate the performance of TB detection based on computerised chest radiography (CXR) readings.DESIGN: Data from 14 094 subjects were analysed from September 2012 to June 2014. All CXRs were read by a physician and by software. Individuals with TB symptoms or CXR abnormalities according to the physician underwent Xpert® MTB/RIF testing, the remaining persons were considered TB-negative (screening reference). A subset of 200 CXRs was read by an independent human reader (radiological reference). This reader also re-read a subset of the most abnormal cases as identified using the software but read as normal by the physician (discordant cases).RESULTS: A total of 10 755 individuals were included in the analysis, 2534 of whom had a positively assessed CXR; 298 cases were Xpert-positive. Using the screening reference, the area under the receiver operating characteristic curve for software readings was 0.93 (95%CI 0.92-0.94), with a sensitivity of 0.98 (95%CI 0.97-0.99) and a specificity of 0.69 (95%CI 0.40-0.98). Based on the radiological reference, the physician performed slightly worse than the software (sensitivity, 0.82, 95%CI 0.74-0.89 and specificity, 0.87, 95%CI 0.81-0.96 vs. sensitivity, 0.83, 95%CI 0.71-0.93 and specificity, 0.87, 95%CI 0.75-0.95), although this was not statistically significant. Of the 291 discordant cases, 70% were assessed as positive, resulting in a 22% increase in TB detection when extrapolated to the full cohort.CONCLUSION: The performance of automated CXR reading is comparable to that of the attending physicians in DetecTB, and its use as a second reader could increase TB detection.

Medial collateral ligament laxity in valgus knee deformity before and after medial closing wedge high tibial osteotomy measured with instrumented laxity measurements and patient reported outcome.

INTRODUCTION: Medial closing wedge high tibial osteotomy (CWHTO) for valgus deformity correction was first described by Coventry whom performed an additional reefing of the medial collateral ligament (MCL) to prevent instability postoperative. In our clinic the additional reefing procedure has never been performed and instability has not been reported routinely by patients. Using instrumented laxity testing, pre- and postoperative valgus and varus knee laxity can be measured objectively. We hypothesize that absence of changes in laxity testing and subjective knee stability scores support that no additional reefing procedure is necessary. MATERIALS AND METHODS: In a prospective cohort study 11 consecutive patients indicated for medial CWHTO were subjected to pre- and postoperative stress X-rays in 30° and 70° of flexion and opening of the joint line was measured in degrees on the radiographs. Patient reported outcome scores were documented with the KOOS, Lysholm, SF36, Oxford Knee Score and a VAS instability scoring tool. RESULTS: All patients (7 females) completed the study, mean age was 46 years. Mean preoperative Hip Knee Ankle angle 6.4° valgus was corrected to mean postoperative alignment 0.1° valgus. A significant difference was measured between mean pre- and postoperative 30° valgus laxity (2.8° vs 5.3°, P = 0.005), 30° varus laxity (6.7° vs 3.2°, P = 0.005) and 70° valgus laxity (2.0° vs 4.8°, P = 0.008). Postoperative patient-reported knee instability as measured with the Lysholm questionnaire was significantly improved compared to preoperative instability (P = 0.006). VAS instability improved, but didn't reach significance (8.0 preoperative and 5.5 postoperative (P = 0.127). Other outcome measures showed improvement as well. No correlations between radiological findings and outcome scores were found. CONCLUSION: A significant increase in postoperative valgus laxity in 30° and 70° of flexion deems reconsidering addition of MCL reefingplasty to the medial CWHTO although patient reported outcome on subjective stability scores fails to report increase of instability in this study population. Instrumented laxity measurements of medial CWHTO patients treated with additional medial reefingplasty should be performed to prove the value of this procedure.

Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus.

BACKGROUND: Diabetes mellitus is a significant risk factor for tuberculosis (TB). We evaluated the performance of computer-aided detection for tuberculosis (CAD4TB) in people living with diabetes mellitus (PLWD) in Indonesia. METHODS: PLWD underwent symptom screening and chest X-ray (CXR); sputum was examined in those with positive symptoms and/or CXR. Digital CXRs were scored using CAD4TB and analysed retrospectively using clinical and microbiological diagnosis as a reference. The area under the receiver operator curve (AUC) of CAD4TB scores was determined, and an optimal threshold score established. Agreement between CAD4TB and the radiologist's reading was determined. RESULTS: Among 346 included PLWD, seven (2.0%) had microbiologically confirmed and two (0.6%) had clinically diagnosed TB. The highest agreement of CAD4TB with radiologist reading was achieved using a threshold score of 70 (κ = 0.41, P < 0.001). The AUC for CAD4TB was 0.89 (95%CI 0.73-1.00). A threshold score of 65 for CAD4TB resulted in a sensitivity, specificity, positive predictive value and negative predictive value of respectively 88.9% (95%CI 51.8-99.7), 88.5% (95%CI 84.6-91.7), 17.0% (95%CI 7.6-30.8) and 99.6% (95%CI 98.2-100). With this threshold, 48 (13.9%) individuals needed microbiological examination and no microbiologically confirmed cases were missed. CONCLUSIONS: CAD4TB has potential as a triage tool for TB screening in PLWD, thereby significantly reducing the need for microbiological examination.

Small airway segmentation in thoracic computed tomography scans: a machine learning approach.

Small airway obstruction is a main cause for chronic obstructive pulmonary disease (COPD). We propose a novel method based on machine learning to extract the airway system from a thoracic computed tomography (CT) scan. The emphasis of the proposed method is on including the smallest airways that are still visible on CT. We used an optimized sampling procedure to extract airway and non-airway voxel samples from a large set of scans for which a semi-automatically constructed reference standard was available. We created a set of features which represent tubular and texture properties that are characteristic for small airway voxels. A random forest classifier was used to determine for each voxel if it belongs to the airway class. Our method was validated on a set of 20 clinical thoracic CT scans from the COPDGene study. Experiments show that our method is effective in extracting the full airway system and in detecting a large number of small airways that were missed by the semi-automatically constructed reference standard.

Accuracy of an automated system for tuberculosis detection on chest radiographs in high-risk screening.

SETTING: Tuberculosis (TB) screening programmes can be optimised by reducing the number of chest radiographs (CXRs) requiring interpretation by human experts. OBJECTIVE: To evaluate the performance of computerised detection software in triaging CXRs in a high-throughput digital mobile TB screening programme. DESIGN: A retrospective evaluation of the software was performed on a database of 38 961 postero-anterior CXRs from unique individuals seen between 2005 and 2010, 87 of whom were diagnosed with TB. The software generated a TB likelihood score for each CXR. This score was compared with a reference standard for notified active pulmonary TB using receiver operating characteristic (ROC) curve and localisation ROC (LROC) curve analyses. RESULTS: On ROC curve analysis, software specificity was 55.71% (95%CI 55.21-56.20) and negative predictive value was 99.98% (95%CI 99.95-99.99), at a sensitivity of 95%. The area under the ROC curve was 0.90 (95%CI 0.86-0.93). Results of the LROC curve analysis were similar. CONCLUSION: The software could identify more than half of the normal images in a TB screening setting while maintaining high sensitivity, and may therefore be used for triage.

Corrigendum: Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

This corrects the article DOI: 10.1038/srep46479.

Automatic versus human reading of chest X-rays in the Zambia National Tuberculosis Prevalence Survey.

SETTING: Tuberculosis (TB) prevalence survey in Zambia between 2013 and 2014. OBJECTIVE: To compare the performance of automatic software (CAD4TB 5) in chest X-ray (CXR) reading with that of field (general practitioners) and central (radiologists) readers. DESIGN: A retrospective study comparing the performance of human and automatic reading was conducted. Two scenarios for central reading were evaluated: abnormalities not consistent with TB were considered to be 'normal' or 'abnormal'. Sputum culture was defined as the reference standard. Measures derived from receiver operating characteristic analysis were used to assess readers' performances. RESULTS: Of 46 099 participants, 23 838 cases included all survey information; of these, 106 cases were culture-confirmed TB-positive. The performance of CAD4TB 5 was similar to that of field and central readers. Although there were significant differences in specificity when compared with field readings (P = 0.002) and central readings considering the first scenario (P < 0.001), these differences were not substantial (93.2% vs. 92.6% and 98.4% vs. 99.6%, respectively).CONCLUSIONp: The performance of automatic CXR readings is comparable with that of human experts in a TB prevalence survey setting using culture as reference.

Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data.

Automated lung lobe segmentation methods often fail for challenging and clinically relevant cases with incomplete fissures or substantial amounts of pathology. We present a fast and intuitive method to interactively correct a given lung lobe segmentation or to quickly create a lobe segmentation from scratch based on a lung mask. A given lobar boundary is converted into a mesh by principal component analysis of 3D lobar boundary markers to obtain a plane where nodes correspond to the position of the markers. An observer can modify the mesh by drawing on 2D slices in arbitrary orientations. After each drawing, the mesh is immediately adapted in a 3D region around the user interaction. For evaluation we participated in the international lung lobe segmentation challenge LObe and lung analysis 2011 (LOLA11). Two observers applied the method to correct a given lung lobe segmentation obtained by a fully automatic method for all 55 CT scans of LOLA11. On average observer 1/2 required 8 ± 4/25 ± 12 interactions per case and took 1:30 ± 0:34/3:19 ± 1:29 min. The average distances to the reference segmentation were improved from an initial 2.68 ± 14.71 mm to 0.89 ± 1.63/0.74 ± 1.51 mm. In addition, one observer applied the proposed method to create a segmentation from scratch. This took 3:44 ± 0:58 minutes on average per case, applying an average of 20 ± 3 interactions to reach an average distance to the reference of 0.77 ± 1.14 mm. Thus, both the interactive corrections and the creation of a segmentation from scratch were feasible in a short time with excellent results and only little interaction. Since the mesh adaptation is independent of image features, the method can successfully handle patients with severe pathologies, provided that the human operator is capable of correctly indicating the lobar boundaries.

Distal femoral valgus osteotomy: bone healing time in single plane and biplanar technique.

Varus deformity can be localized in the tibia, in the femur or in both. If varus deformity is localized within the femur, it is mandatory to correct it in the femur. This report presents the technique and results of a consecutive case series of lateral uniplanar and biplanar closed-wedge valgus osteotomy of the distal femur for the treatment of varus deformity of the knee. Retrospectively, fifteen patients (sixteen knees) were identified. Indications for surgery varied from unloading an osteoarthritic medial compartment to reduction to symmetrical varus leg alignment. Pre- and post-operative X-rays, including a full leg radiograph, were assessed as well as bone healing time at follow-up intervals. Clinical outcome was assessed using different questionnaires. There were nine male and six female patients with a median age at surgery of 45 (±14) years. The mLDFA changed from 95.9° (±2.7°) preoperatively to 89.3° (±2.9°) post-operatively. Preoperative planning and the use of angle stable implants resulted in accurate corrections according to preoperative aims in all but one patient. At follow-up (mean, 40 months), the mean VAS score was 2.5 (±2.4) and the WOMAC score averaged 80 (±20). The mean bone healing time of biplanar osteotomies (4 ± 3 months) was shorter than in the uniplanar osteotomies (6 ± 3 months). Distal lateral closed-wedge valgus osteotomy of the femur for the treatment of femoral varus deformities resulted in clinical improvement and accurate corrections in patients with different aims for correction. A biplanar osteotomy technique shortens bone healing time.

Automated chest-radiography as a triage for Xpert testing in resource-constrained settings: a prospective study of diagnostic accuracy and costs.

Molecular tests hold great potential for tuberculosis (TB) diagnosis, but are costly, time consuming, and HIV-infected patients are often sputum scarce. Therefore, alternative approaches are needed. We evaluated automated digital chest radiography (ACR) as a rapid and cheap pre-screen test prior to Xpert MTB/RIF (Xpert). 388 suspected TB subjects underwent chest radiography, Xpert and sputum culture testing. Radiographs were analysed by computer software (CAD4TB) and specialist readers, and abnormality scores were allocated. A triage algorithm was simulated in which subjects with a score above a threshold underwent Xpert. We computed sensitivity, specificity, cost per screened subject (CSS), cost per notified TB case (CNTBC) and throughput for different diagnostic thresholds. 18.3% of subjects had culture positive TB. For Xpert alone, sensitivity was 78.9%, specificity 98.1%, CSS $13.09 and CNTBC $90.70. In a pre-screening setting where 40% of subjects would undergo Xpert, CSS decreased to $6.72 and CNTBC to $54.34, with eight TB cases missed and throughput increased from 45 to 113 patients/day. Specialists, on average, read 57% of radiographs as abnormal, reducing CSS ($8.95) and CNTBC ($64.84). ACR pre-screening could substantially reduce costs, and increase daily throughput with few TB cases missed. These data inform public health policy in resource-constrained settings.

Timing-Invariant CT Angiography Derived from CT Perfusion Imaging in Acute Stroke: A Diagnostic Performance Study.

BACKGROUND AND PURPOSE: Timing-invariant (or delay-insensitive) CT angiography derived from CT perfusion data may obviate a separate cranial CTA in acute stroke, thus enhancing patient safety by reducing total examination time, radiation dose, and volume of contrast material. We assessed the diagnostic accuracy of timing-invariant CTA for detecting intracranial artery occlusion in acute ischemic stroke, to examine whether standard CTA can be omitted. MATERIALS AND METHODS: Patients with suspected ischemic stroke were prospectively enrolled and underwent CTA and CTP imaging at admission. Timing-invariant CTA was derived from the CTP data. Five neuroradiologic observers assessed all images for the presence and location of intracranial artery occlusion in a blinded and randomized manner. Sensitivity and specificity of timing-invariant CTA and standard CTA were calculated by using an independent expert panel as the reference standard. Interrater agreement was determined by using κ statistics. RESULTS: We included 108 patients with 47 vessel occlusions. Overall, standard CTA and timing-invariant CTA provided similar high diagnostic accuracy for occlusion detection with a sensitivity of 96% (95% CI, 90%-100%) and a specificity of 100% (99%-100%) for standard CTA and a sensitivity of 98% (95% CI, 94%-100%) and a specificity of 100% (95% CI, 100%-100%) for timing-invariant CTA. For proximal large-vessel occlusions, defined as occlusions of the ICA, basilar artery, and M1, the sensitivity and specificity were 100% (95% CI, 100%-100%) for both techniques. Interrater agreement was good for both techniques (mean κ value, 0.75 and 0.76). CONCLUSIONS: Timing-invariant CTA derived from CTP data provides diagnostic accuracy similar to that of standard CTA for the detection of artery occlusions in acute stroke.

Localized Energy-Based Normalization of Medical Images: Application to Chest Radiography.

Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR). The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed. We investigate iterative and local application of this technique. The normalization is applied iteratively to the lung fields on six datasets from different sources, each comprising 50 normal CXRs and 50 abnormal CXRs. The method is evaluated in three supervised computer-aided detection tasks related to CXR analysis and compared to two reference normalization methods. In the first task, automatic lung segmentation, the average Jaccard overlap significantly increased from 0.72±0.30 and 0.87±0.11 for both reference methods to with normalization. The second experiment was aimed at segmentation of the clavicles. The reference methods had an average Jaccard index of 0.57±0.26 and 0.53±0.26; with normalization this significantly increased to . The third experiment was detection of tuberculosis related abnormalities in the lung fields. The average area under the Receiver Operating Curve increased significantly from 0.72±0.14 and 0.79±0.06 using the reference methods to with normalization. We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources.

Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans.

The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.

Screening for pulmonary tuberculosis in a Tanzanian prison and computer-aided interpretation of chest X-rays.

SETTING: Tanzania is a high-burden country for tuberculosis (TB), and prisoners are a high-risk group that should be screened actively, as recommended by the World Health Organization. Screening algorithms, starting with chest X-rays (CXRs), can detect asymptomatic cases, but depend on experienced readers, who are scarce in the penitentiary setting. Recent studies with patients seeking health care for TB-related symptoms showed good diagnostic performance of the computer software CAD4TB. OBJECTIVE: To assess the potential of computer-assisted screening using CAD4TB in a predominantly asymptomatic prison population. DESIGN: Cross-sectional study. RESULTS: CAD4TB and seven health care professionals reading CXRs in local tuberculosis wards evaluated a set of 511 CXRs from the Ukonga prison in Dar es Salaam. Performance was compared using a radiological reference. Two readers performed significantly better than CAD4TB, three were comparable, and two performed significantly worse (area under the curve 0.75 in receiver operating characteristics analysis). On a superset of 1321 CXRs, CAD4TB successfully interpreted >99%, with a predictably short time to detection, while 160 (12.2%) reports were delayed by over 24 h with conventional CXR reading. CONCLUSION: CAD4TB reliably evaluates CXRs from a mostly asymptomatic prison population, with a diagnostic performance inferior to that of expert readers but comparable to local readers.

Contexte : La Tanzanie est lourdement frappée par la tuberculose (TB) et les prisonniers sont un groupe à haut risque qui devrait bénéficier d'un dépistage actif, comme le recommande l'Organisation Mondiale de la Santé. Les algorithmes de dépistage qui débutent par une radiographie pulmonaire peuvent détecter des cas asymptomatiques, mais ils requièrent des lecteurs de radiographies expérimentés, qui sont rares dans le contexte pénitentiaire. Des études récentes sur des patients sollicitant des soins pour des symptômes liés à la TB ont mis en évidence une bonne performance diagnostique du logiciel CAD4TB.Objectif : Evaluer le potentiel d'un dépistage assisté par ordinateur en utilisant CAD4TB au sein d'une population carcérale en majorité asymptomatique.Schéma : Étude transversale.Résultats : CAD4TB et sept professionnels de santé lisant des radiographies dans des services de TB locaux ont évalué un ensemble de 511 radiographies pulmonaires provenant de la prison d'Ukonga à Dar es Salaam et les performances ont été comparées grâce à une radiographie de référence. Deux lecteurs ont été significativement plus performants que CAD4TB, trois ont été comparables et deux ont été significativement moins bons (zone sous la courbe de 0,75 dans l'analyse ROC ­fonction d'efficacité du receveur). Sur un ensemble de 1321 radiographies pulmonaires, CAD4TB en a interprété avec succès plus de 99% avec un délai de détection prévisible court, tandis que 160 (12,2%) réponses ont été retardées de plus de 24 h avec la méthode de lecture conventionnelle.Conclusion : CAD4TB évalue de manière fiable les radiographies pulmonaires dans une population en majorité asymptomatique de détenus, avec une performance diagnostique inférieure à celle de lecteurs experts mais comparable à celle des lecteurs locaux.

Marco de referencia: Tanzania es un país con una alta tasa de morbilidad por tuberculosis (TB) y las personas en los establecimientos penitenciarios constituyen un grupo de alto riesgo de contraer la enfermedad; en esta población se debe practicar la detección sistemática activa como lo recomienda la Organización Mundial de la Salud. Los algoritmos de detección cuya etapa inicial es la radiografía de tórax pueden detectar los casos asintomáticos, pero su eficacia depende de la experiencia del profesional que interpreta las imágenes y esta competencia es escasa en los entornos penitenciarios. Algunos estudios recientes de pacientes que buscan atención sanitaria por síntomas asociados con la TB han revelado un buen rendimiento diagnóstico con la utilización del programa informático CAD4TB. Objetivo: Evaluar la utilidad de la detección sistemática de la TB asistida por el programa CAD4TB, en una población penitenciaria en su mayoría asintomática.Método: Fue este un estudio de tipo transversal.Resultados: Siete profesionales de atención sanitaria de los servicios locales de TB analizaron 511 radiografías de tórax provenientes de la prisión de Ukonga, en Dar es-Salam, con la ayuda del programa CAD4TB; se preparó un conjunto de referencia radiográfica de lectura con el fin de evaluar el rendimiento diagnóstico. El desempeño de dos de los lectores fue significativamente superior al resultado del programa CAD4TB, tres lectores obtuvieron una puntuación comparable al programa y en dos lectores se observó un rendimiento significativamente inferior (área bajo la curva: 0,75 en el análisis de eficacia diagnóstica). En un conjunto especial de 1321 radiografías de tórax el programa CAD4TB interpretó eficazmente más del 99%, con un corto lapso previsible hasta la detección, en contraste con la lectura clásica de las radiografías que dio lugar a un retraso superior a 24 horas en 160 informes (12,2%).Conclusión: El programa CAD4TB realizó una evaluación fiable de las radiografías provenientes de una población penitenciaria en su mayor parte asintomática. El rendimiento diagnóstico del programa fue inferior al rendimiento de los lectores expertos, pero comparable con el rendimiento de los lectores locales.

New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

OBJECTIVE: To investigate two new methods of using computer-aided detection (CAD) system information for the detection of lung nodules on chest radiographs. We evaluated an interactive CAD application and an independent combination of radiologists and CAD scores. METHODS: 300 posteroanterior and lateral digital chest radiographs were selected, including 111 with a solitary pulmonary nodule (average diameter, 16 mm). Both nodule and control cases were verified by CT. Six radiologists and six residents reviewed the chest radiographs without CAD and with CAD (ClearRead +Detect™ 5.2; Riverain Technologies, Miamisburg, OH) in two reading sessions. The CAD system was used in an interactive manner; CAD marks, accompanied by a score of suspicion, remained hidden unless the location was queried by the radiologist. Jackknife alternative free response receiver operating characteristics multireader multicase analysis was used to measure detection performance. Area under the curve (AUC) and partial AUC (pAUC) between a specificity of 80% and 100% served as the measure for detection performance. We also evaluated the results of a weighted combination of CAD scores and reader scores, at the location of reader findings. RESULTS: AUC for the observers without CAD was 0.824. No significant improvement was seen with interactive use of CAD (AUC = 0.834; p = 0.15). Independent combination significantly improved detection performance (AUC = 0.834; p = 0.006). pAUCs without and with interactive CAD were similar (0.128), but improved with independent combination (0.137). CONCLUSION: Interactive CAD did not improve reader performance for the detection of lung nodules on chest radiographs. Independent combination of reader and CAD scores improved the detection performance of lung nodules. ADVANCES IN KNOWLEDGE: (1) Interactive use of currently available CAD software did not improve the radiologists' detection performance of lung nodules on chest radiographs. (2) Independently combining the interpretations of the radiologist and the CAD system improved detection of lung nodules on chest radiographs.