Corrigendum to "An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD" [Kidney International Reports Volume 9, Issue 2, February 2024, Pages 249-256].

[This corrects the article DOI: 10.1016/j.ekir.2023.10.029.].

An Artificial Intelligence Generated Automated Algorithm to Measure Total Kidney Volume in ADPKD.

Introduction: Accurate tools to inform individual prognosis in patients with autosomal dominant polycystic kidney disease (ADPKD) are lacking. Here, we report an artificial intelligence (AI)-generated method for routinely measuring total kidney volume (TKV). Methods: An ensemble U-net algorithm was created using the nnUNet approach. The training and internal cross-validation cohort consisted of all 1.5T magnetic resonance imaging (MRI) data acquired using 5 different MRI scanners (454 kidneys, 227 scans) in the CYSTic consortium, which was first manually segmented by a single human operator. As an independent validation cohort, we utilized 48 sequential clinical MRI scans with reference results of manual segmentation acquired by 6 individual analysts at a single center. The tool was then implemented for clinical use and its performance analyzed. Results: The training or internal validation cohort was younger (mean age 44.0 vs. 51.5 years) and the female-to-male ratio higher (1.2 vs. 0.94) compared to the clinical validation cohort. The majority of CYSTic patients had PKD1 mutations (79%) and typical disease (Mayo Imaging class 1, 86%). The median DICE score on the clinical validation data set between the algorithm and human analysts was 0.96 for left and right kidneys with a median TKV error of -1.8%. The time taken to manually segment kidneys in the CYSTic data set was 56 (±28) minutes, whereas manual corrections of the algorithm output took 8.5 (±9.2) minutes per scan. Conclusion: Our AI-based algorithm demonstrates performance comparable to manual segmentation. Its rapidity and precision in real-world clinical cases demonstrate its suitability for clinical application.

Evaluating the performance of artificial intelligence software for lung nodule detection on chest radiographs in a retrospective real-world UK population.

OBJECTIVES: Early identification of lung cancer on chest radiographs improves patient outcomes. Artificial intelligence (AI) tools may increase diagnostic accuracy and streamline this pathway. This study evaluated the performance of commercially available AI-based software trained to identify cancerous lung nodules on chest radiographs. DESIGN: This retrospective study included primary care chest radiographs acquired in a UK centre. The software evaluated each radiograph independently and outputs were compared with two reference standards: (1) the radiologist report and (2) the diagnosis of cancer by multidisciplinary team decision. Failure analysis was performed by interrogating the software marker locations on radiographs. PARTICIPANTS: 5722 consecutive chest radiographs were included from 5592 patients (median age 59 years, 53.8% women, 1.6% prevalence of cancer). RESULTS: Compared with radiologist reports for nodule detection, the software demonstrated sensitivity 54.5% (95% CI 44.2% to 64.4%), specificity 83.2% (82.2% to 84.1%), positive predictive value (PPV) 5.5% (4.6% to 6.6%) and negative predictive value (NPV) 99.0% (98.8% to 99.2%). Compared with cancer diagnosis, the software demonstrated sensitivity 60.9% (50.1% to 70.9%), specificity 83.3% (82.3% to 84.2%), PPV 5.6% (4.8% to 6.6%) and NPV 99.2% (99.0% to 99.4%). Normal or variant anatomy was misidentified as an abnormality in 69.9% of the 943 false positive cases. CONCLUSIONS: The software demonstrated considerable underperformance in this real-world patient cohort. Failure analysis suggested a lack of generalisability in the training and testing datasets as a potential factor. The low PPV carries the risk of over-investigation and limits the translation of the software to clinical practice. Our findings highlight the importance of training and testing software in representative datasets, with broader implications for the implementation of AI tools in imaging.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.

Introduction: Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA. Methods: A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort. Results: Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases. Conclusion: Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

Individualized everolimus treatment for tuberous sclerosis-related angiomyolipoma promotes treatment adherence and response.

Background: Everolimus is a potential alternative to embolization and nephrectomy for managing tuberous sclerosis complex (TSC)-associated renal angiomyolipoma (AML). In 2016, National Health Service England approved its use through regional centres for renal AML ≥30 mm showing interval growth. Evidence of lesion stabilization or reduction after 6 months is mandated for continuation of long-term treatment. Methods: From November 2016 to June 2021, all potentially eligible adult TSC patients with AML across Yorkshire and Humber were referred for assessment and monitoring. Eligible patients underwent baseline renal magnetic resonance imaging (MRI) assessment and a follow-up MRI scan after 6 months on everolimus. Dose titration was guided by trough levels and lesion responsiveness using a new 3D MRI volumetric protocol. Results: Of 28 patients commencing treatment, 19 tolerated everolimus for >3 months. Overall, 11 patients (40%) discontinued treatment, mostly due to recurrent infections (42%) and allergic reactions (25%). Sixty-eight percent required dose adjustments from the initiating dose (10 mg) due to sub-optimal trough levels (38%), minimal AML response (15%) or adverse events (47%). 3D volumetric assessment confirmed a reduction in AML volume of a pre-selected index lesion in all treatment-naïve cases (n = 14), showing superiority over 2D measurements of lesion diameter. Conclusion: In this cohort, everolimus promoted AML regression in all patients who tolerated the drug for >6 months with stabilization observed over 3 years. Trough levels enabled individual dose titration to maximize responsiveness and minimize side effects. The use of 3D MRI assessment of lesion volume was superior to 2D measurements of lesion diameter in monitoring treatment response.

Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction.

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of patients, including a broad spectrum of right ventricular and left ventricular conditions, with internal and external testing. Fully automatic cardiac MRI assessment correlated strongly with invasive hemodynamics, had prognostic value, were highly repeatable, and showed excellent generalizability. Clinical trial registration no. NCT03841344 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Ambale-Venkatesh and Lima in this issue. An earlier incorrect version appeared online. This article was corrected on June 27, 2022.

Computed tomography lung parenchymal descriptions in routine radiological reporting have diagnostic and prognostic utility in patients with idiopathic pulmonary arterial hypertension and pulmonary hypertension associated with lung disease.

BACKGROUND: Patients with pulmonary hypertension (PH) and lung disease may pose a diagnostic dilemma between idiopathic pulmonary arterial hypertension (IPAH) and PH associated with lung disease (PH-CLD). The prognostic impact of common computed tomography (CT) parenchymal features is unknown. METHODS: 660 IPAH and PH-CLD patients assessed between 2001 and 2019 were included. Reports for all CT scans 1 year prior to diagnosis were analysed for common lung parenchymal patterns. Cox regression and Kaplan-Meier analysis were performed. RESULTS: At univariate analysis of the whole cohort, centrilobular ground-glass (CGG) changes (hazard ratio, HR 0.29) and ground-glass opacification (HR 0.53) predicted improved survival, while honeycombing (HR 2.79), emphysema (HR 2.09) and fibrosis (HR 2.38) predicted worse survival (all p<0.001). Fibrosis was an independent predictor after adjusting for baseline demographics, PH severity and diffusing capacity of the lung for carbon monoxide (HR 1.37, p<0.05). Patients with a clinical diagnosis of IPAH who had an absence of reported parenchymal lung disease (IPAH-noLD) demonstrated superior survival to patients diagnosed with either IPAH who had coexistent CT lung disease or PH-CLD (2-year survival of 85%, 60% and 46%, respectively, p<0.05). CGG changes were present in 23.3% of IPAH-noLD and 5.8% of PH-CLD patients. There was no significant difference in survival between IPAH-noLD patients with or without CGG changes. PH-CLD patients with fibrosis had worse survival than those with emphysema. INTERPRETATION: Routine clinical reports of CT lung parenchymal disease identify groups of patients with IPAH and PH-CLD with significantly different prognoses. Isolated CGG changes are not uncommon in IPAH but are not associated with worse survival.

Cardiac stereotactic ablative radiotherapy for control of refractory ventricular tachycardia: initial UK multicentre experience.

BACKGROUND: Options for patients with ventricular tachycardia (VT) refractory to antiarrhythmic drugs and/or catheter ablation remain limited. Stereotactic radiotherapy has been described as a novel treatment option. METHODS: Seven patients with recurrent refractory VT, deemed high risk for either first time or redo invasive catheter ablation, were treated across three UK centres with non-invasive cardiac stereotactic ablative radiotherapy (SABR). Prior catheter ablation data and non-invasive mapping were combined with cross-sectional imaging to generate radiotherapy plans with aim to deliver a single 25 Gy treatment. Shared planning and treatment guidelines and prospective peer review were used. RESULTS: Acute suppression of VT was seen in all seven patients. For five patients with at least 6 months follow-up, overall reduction in VT burden was 85%. No high-grade radiotherapy treatment-related side effects were documented. Three deaths (two early, one late) occurred due to heart failure. CONCLUSIONS: Cardiac SABR showed reasonable VT suppression in a high-risk population where conventional treatment had failed.

Custom-made 3D printed masks for children using non-invasive ventilation: a comparison of 3D scanning technologies and specifications for future clinical service use, guided by patient and professional experience.

Non-invasive ventilation (NIV) is assisted mechanical ventilation delivered via a facemask for people with chronic conditions that affect breathing. Mass-produced masks are available for both the adult and paediatric markets but masks that fit well are difficult to find for children who are small or have asymmetrical facial features. A good fit between the mask and the patient's face to minimise unintentional air leakage is essential to deliver the treatment effectively. We present an innovative use of 3D assessment and manufacturing technologies to deliver novel custom-made facemasks for children for whom a well-fitting standard mask is not available. This paper aims to describe the processes undertaken to investigate and compare currently available technologies for 3D scanning children and to explore the design of a system for creating custom-made paediatric NIV masks within the NHS. The paper therefore considers not only the quality and accuracy of the data, but also other factors such as the time and ease of process. Searches for all currently available scanning technologies were made. Photogrammetry image stitch using a smartphone and a digital camera, and two structured light scanners were selected and compared in the laboratory, in discussion with user groups, and in adult volunteers. Using the processes described, it became apparent that the optimal 3D scanning system for this purpose was the handheld structured light scanner. This option offered both superior accuracy and convenience and was more cost effective.

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review.

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

COVID-19: A systematic evaluation of personal protective equipment (PPE) performance during restraint.

BACKGROUND: Restraint is widely practised within inpatient mental health services and is considered a higher-risk procedure for patients and staff. There is a sparsity of evidence in respect of the efficacy of personal protective equipment (PPE) used during restraint for reducing risk of infection. METHODS: A series of choreographed restraint episodes were used to simulate contact contamination in research participants playing the roles of staff members and a patient. For comparison, one episode of simulated recording of physical observations was taken. Ultraviolet (UV) fluorescent material was used to track the simulated contact contamination, with analysis undertaken using established image registration techniques of UV photographs. This was repeated for three separate sets of PPE. RESULTS: All three PPE sets showed similar performance in protecting against contamination transfer. For teams not utilising coveralls, this was dependent upon effective cleansing as part of doffing. There were similar patterns of contamination for restraint team members assigned to specific roles, with hands and upper torso appearing to be higher-risk areas. The restraint-related contamination was 23 times higher than that observed for physical observations. DISCUSSION: A second layer of clothing that can be removed showed efficacy in reducing contact contamination. PPE fit to individual is important. Post-restraint cleansing procedures are currently inadequate, with new procedures for face and neck cleansing required. These findings leave scope for staff to potentially improve their appearance when donning PPE and engaging with distressed patients.

Left ventricular fibrosis and hypertrophy are associated with mortality in heart failure with preserved ejection fraction.

Cardiac magnetic resonance (CMR) is emerging as an important tool in the assessment of heart failure with preserved ejection fraction (HFpEF). This study sought to investigate the prognostic value of multiparametric CMR, including left and right heart volumetric assessment, native T1-mapping and LGE in HFpEF. In this retrospective study, we identified patients with HFpEF who have undergone CMR. CMR protocol included: cines, native T1-mapping and late gadolinium enhancement (LGE). The mean follow-up period was 3.2 ± 2.4 years. We identified 86 patients with HFpEF who had CMR. Of the 86 patients (85% hypertensive; 61% males; 14% cardiac amyloidosis), 27 (31%) patients died during the follow up period. From all the CMR metrics, LV mass (area under curve [AUC] 0.66, SE 0.07, 95% CI 0.54-0.76, p = 0.02), LGE fibrosis (AUC 0.59, SE 0.15, 95% CI 0.41-0.75, p = 0.03) and native T1-values (AUC 0.76, SE 0.09, 95% CI 0.58-0.88, p < 0.01) were the strongest predictors of all-cause mortality. The optimum thresholds for these were: LV mass > 133.24 g (hazard ratio [HR] 1.58, 95% CI 1.1-2.2, p < 0.01); LGE-fibrosis > 34.86% (HR 1.77, 95% CI 1.1-2.8, p = 0.01) and native T1 > 1056.42 ms (HR 2.36, 95% CI 0.9-6.4, p = 0.07). In multivariate cox regression, CMR score model comprising these three variables independently predicted mortality in HFpEF when compared to NTproBNP (HR 4 vs HR 1.65). In non-amyloid HFpEF cases, only native T1 > 1056.42 ms demonstrated higher mortality (AUC 0.833, p < 0.01). In patients with HFpEF, multiparametric CMR aids prognostication. Our results show that left ventricular fibrosis and hypertrophy quantified by CMR are associated with all-cause mortality in patients with HFpEF.

A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis.

AIMS: Pulmonary arterial hypertension (PAH) is a progressive condition with high mortality. Quantitative cardiovascular magnetic resonance (CMR) imaging metrics in PAH target individual cardiac structures and have diagnostic and prognostic utility but are challenging to acquire. The primary aim of this study was to develop and test a tensor-based machine learning approach to holistically identify diagnostic features in PAH using CMR, and secondarily, visualize and interpret key discriminative features associated with PAH. METHODS AND RESULTS: Consecutive treatment naive patients with PAH or no evidence of pulmonary hypertension (PH), undergoing CMR and right heart catheterization within 48 h, were identified from the ASPIRE registry. A tensor-based machine learning approach, multilinear subspace learning, was developed and the diagnostic accuracy of this approach was compared with standard CMR measurements. Two hundred and twenty patients were identified: 150 with PAH and 70 with no PH. The diagnostic accuracy of the approach was high as assessed by area under the curve at receiver operating characteristic analysis (P < 0.001): 0.92 for PAH, slightly higher than standard CMR metrics. Moreover, establishing the diagnosis using the approach was less time-consuming, being achieved within 10 s. Learnt features were visualized in feature maps with correspondence to cardiac phases, confirming known and also identifying potentially new diagnostic features in PAH. CONCLUSION: A tensor-based machine learning approach has been developed and applied to CMR. High diagnostic accuracy has been shown for PAH diagnosis and new learnt features were visualized with diagnostic potential.

Custom-made 3D printed masks for children using non-invasive ventilation: a feasibility study of production method and testing of outcomes in adult volunteers.

Non-invasive ventilation (NIV) is assisted mechanical ventilation delivered via a facemask for people with chronic conditions that affect breathing. NIV is most commonly delivered via an interface (mask) covering the nose (nasal mask) or the nose and mouth (oronasal mask). The number of children in the UK requiring NIV is currently estimated to be around 5000. Mass-produced masks are available for both the adult and paediatric markets but masks that fit well are difficult to find for children who are small or have asymmetrical facial features. A good conforming fit between the mask and the patient's face to minimise unintentional air leakage is essential to deliver the treatment effectively; most ventilators will trigger an alarm requiring action if such leakage is detected. We present an innovative use of 3D scanning and manufacturing technologies to deliver novel mask-face interfaces to optimise mask fit to the needs of individual patients. Ahead of planned user trials with paediatric patients, the project team trialled the feasibility of the process of creating and printing bespoke masks from 3D scan data and carried out testing of the masks in adult volunteers to select the strongest design concept for the paediatric trial. The evaluation of the process of designing a bespoke mask from scan data, arranging for its manufacture and carrying out user testing has been invaluable in gaining knowledge and discovering the pitfalls and timing bottlenecks in the processes. This allowed the team to iteratively refine the techniques and methods involved, informing user trials later on in the project. It has also provided indicative cost estimates for 3D printed mask prototype components which are useful in project decision making and trial planning. The value of the process extends to considerations for future implementation of the process within a clinical pathway.

A rapid high-performance semi-automated tool to measure total kidney volume from MRI in autosomal dominant polycystic kidney disease.

OBJECTIVES: To develop a high-performance, rapid semi-automated method (Sheffield TKV Tool) for measuring total kidney volume (TKV) from magnetic resonance images (MRI) in patients with autosomal dominant polycystic kidney disease (ADPKD). METHODS: TKV was initially measured in 61 patients with ADPKD using the Sheffield TKV Tool and its performance compared to manual segmentation and other published methods (ellipsoidal, mid-slice, MIROS). It was then validated using an external dataset of MRI scans from 65 patients with ADPKD. RESULTS: Sixty-one patients (mean age 45 ± 14 years, baseline eGFR 76 ± 32 ml/min/1.73 m2) with ADPKD had a wide range of TKV (258-3680 ml) measured manually. The Sheffield TKV Tool was highly accurate (mean volume error 0.5 ± 5.3% for right kidney, - 0.7 ± 5.5% for left kidney), reproducible (intra-operator variability - 0.2 ± 1.3%; inter-operator variability 1.1 ± 2.9%) and outperformed published methods. It took less than 6 min to execute and performed consistently with high accuracy in an external MRI dataset of T2-weighted sequences with TKV acquired using three different scanners and measured using a different segmentation methodology (mean volume error was 3.45 ± 3.96%, n = 65). CONCLUSIONS: The Sheffield TKV Tool is operator friendly, requiring minimal user interaction to rapidly, accurately and reproducibly measure TKV in this, the largest reported unselected European patient cohort with ADPKD. It is more accurate than estimating equations and its accuracy is maintained at larger kidney volumes than previously reported with other semi-automated methods. It is free to use, can run as an independent executable and will accelerate the application of TKV as a prognostic biomarker for ADPKD into clinical practice. KEY POINTS: • This new semi-automated method (Sheffield TKV Tool) to measure total kidney volume (TKV) will facilitate the routine clinical assessment of patients with ADPKD. • Measuring TKV manually is time consuming and laborious. • TKV is a prognostic indicator in ADPKD and the only imaging biomarker approved by the FDA and EMA.

99mTc white-cell imaging in inflammatory bowel disease: a comparison of planar versus SPECT.

OBJECTIVE: 99mTc-labelled white-cell scintigraphy is sensitive and specific for detecting inflammatory bowel disease (IBD). A retrospective analysis was carried out on 99 consecutive studies to compare results from planar imaging and single photon emission computed tomography (SPECT). MATERIALS AND METHODS: Two observers blindly and independently reported all planar images followed by the SPECTs. Both sets of scans were reported in random order. All possible abnormalities were assigned a confidence rating and specific, preagreed bowel location and their extent measured. RESULTS: The observers disagreed on whether studies were normal in 17 of 99 planar scans (κ=0.56) but only eight of 99 SPECTs (κ=0.83). Consensus reporting, where there was disagreement between observers, yielded the following results: planar and SPECT normal in 56 of 99 cases; planar and SPECT positive in 29 of 99 cases; planar normal but SPECT positive in 13 of 99 cases; planar positive but SPECT normal in one of 99 cases. In the 14 of 99 cases where there was discordance between planar and SPECT, confirmation of active IBD was sought from other investigations (e.g. histology). Relevant other investigations were available in eight of these discordant cases and showed agreement with the SPECT result in seven of eight patients. Where the same abnormalities were identified in both planar and SPECT, there was a significant increase (P<0.001 mean: 57 mm) in the extent of abnormalities as measured from SPECT compared with planar. CONCLUSION: Reporting from SPECT improves inter-operator variability and appears to improve sensitivity for detecting IBD. Planar imaging also appears to underestimate the extent of active disease.