Fully automated volumetric measurement of malignant pleural mesothelioma by deep learning AI: validation and comparison with modified RECIST response criteria.

BACKGROUND: In malignant pleural mesothelioma (MPM), complex tumour morphology results in inconsistent radiological response assessment. Promising volumetric methods require automation to be practical. We developed a fully automated Convolutional Neural Network (CNN) for this purpose, performed blinded validation and compared CNN and human response classification and survival prediction in patients treated with chemotherapy. METHODS: In a multicentre retrospective cohort study; 183 CT datasets were split into training and internal validation (123 datasets (80 fully annotated); 108 patients; 1 centre) and external validation (60 datasets (all fully annotated); 30 patients; 3 centres). Detailed manual annotations were used to train the CNN, which used two-dimensional U-Net architecture. CNN performance was evaluated using correlation, Bland-Altman and Dice agreement. Volumetric response/progression were defined as ≤30%/≥20% change and compared with modified Response Evaluation Criteria In Solid Tumours (mRECIST) by Cohen's kappa. Survival was assessed using Kaplan-Meier methodology. RESULTS: Human and artificial intelligence (AI) volumes were strongly correlated (validation set r=0.851, p<0.0001). Agreement was strong (validation set mean bias +31 cm3 (p=0.182), 95% limits 345 to +407 cm3). Infrequent AI segmentation errors (4/60 validation cases) were associated with fissural tumour, contralateral pleural thickening and adjacent atelectasis. Human and AI volumetric responses agreed in 20/30 (67%) validation cases κ=0.439 (0.178 to 0.700). AI and mRECIST agreed in 16/30 (55%) validation cases κ=0.284 (0.026 to 0.543). Higher baseline tumour volume was associated with shorter survival. CONCLUSION: We have developed and validated the first fully automated CNN for volumetric MPM segmentation. CNN performance may be further improved by enriching future training sets with morphologically challenging features. Volumetric response thresholds require further calibration in future studies.

Comparing performance on the Months of the Year Backwards test in hospitalised patients with delirium, dementia, and no cognitive impairment: an exploratory study.

PURPOSE: To investigate performance of the Months of the Year Backwards (MOTYB) test in older hospitalised patients with delirium, dementia, and no cognitive impairment. METHODS: Secondary analysis of data from a case-control study of 149 hospitalised patients aged ≥ 65 years with delirium [with or without dementia (N = 50)], dementia [without delirium (N = 46)], and no cognitive impairment (N = 53). Verbatim transcripts of MOTYB audio recordings were analysed to determine group differences in response patterns. RESULTS: In the total sample [median age 85y (IQR 80-88), 82% female], patients with delirium were more often unable to recite months backward to November (36/50 = 72%) than patients with dementia (21/46 = 46%; p < 0.01) and both differed significantly from patients without cognitive impairment (2/53 = 4%; p's < 0.001). 121/149 (81%) of patients were able to engage with the test. Patients with delirium were more often unable to engage with MOTYB (23/50 = 46%; e.g., due to reduced arousal) than patients with dementia (5/46 = 11%; p < 0.001); both groups differed significantly (p's < 0.001) from patients without cognitive impairment (0/53 = 0%). There was no statistically significant difference between patients with delirium (2/27 = 7%) and patients with dementia (8/41 = 20%) in completing MOTYB to January, but performance in both groups differed (p < 0.001 and p < 0.02, respectively) from patients without cognitive impairment (35/53 = 66%). CONCLUSION: Delirium was associated with inability to engage with MOTYB and low rates of completion. In patients able to engage with the test, error-free completion rates were low in delirium and dementia. Recording of engagement and patterns of errors may add useful information to MOTYB scoring.

A smartphone-based test for the assessment of attention deficits in delirium: A case-control diagnostic test accuracy study in older hospitalised patients.

BACKGROUND: Delirium is a common and serious acute neuropsychiatric syndrome which is often missed in routine clinical care. Inattention is the core cognitive feature. Diagnostic test accuracy (including cut-points) of a smartphone Delirium App (DelApp) for assessing attention deficits was assessed in older hospital inpatients. METHODS: This was a case-control study of hospitalised patients aged ≥65 years with delirium (with or without pre-existing cognitive impairment), who were compared to patients with dementia without delirium, and patients without cognitive impairment. Reference standard delirium assessment, which included a neuropsychological test battery, was based on Diagnostic and Statistical Manual of Mental Disorders-5 criteria. A separate blinded assessor administered the DelApp arousal assessment (score 0-4) and attention task (0-6) yielding an overall score of 0 to 10 (lower scores indicate poorer performance). Analyses included receiver operating characteristic curves and sensitivity and specificity. Optimal cut-points for delirium detection were determined using Youden's index. RESULTS: A total of 187 patients were recruited, mean age 83.8 (range 67-98) years, 152 (81%) women; n = 61 with delirium; n = 61 with dementia without delirium; and n = 65 without cognitive impairment. Patients with delirium performed poorly on the DelApp (median score = 4/10; inter-quartile range 3.0, 5.5) compared to patients with dementia (9.0; 5.5, 10.0) and those without cognitive impairment (10.0; 10.0, 10.0). Area under the curve for detecting delirium was 0.89 (95% Confidence Interval 0.84, 0.94). At an optimal cut-point of ≤8, sensitivity was 91.7% (84.7%, 98.7%) and specificity 74.2% (66.5%, 81.9%) for discriminating delirium from the other groups. Specificity was 68.3% (56.6%, 80.1%) for discriminating delirium from dementia (cut-point ≤6). CONCLUSION: Patients with delirium (with or without pre-existing cognitive impairment) perform poorly on the DelApp compared to patients with dementia and those without cognitive impairment. A cut-point of ≤8/10 is suggested as having optimal sensitivity and specificity. The DelApp is a promising tool for assessment of attention deficits associated with delirium in older hospitalised adults, many of whom have prior cognitive impairment, and should be further validated in representative patient cohorts.

Diagnostic test accuracy of a novel smartphone application for the assessment of attention deficits in delirium in older hospitalised patients: a prospective cohort study protocol.

BACKGROUND: Delirium is a common and serious clinical syndrome which is often missed in routine clinical care. The core cognitive feature is inattention. We developed a novel bedside neuropsychological test for assessing inattention in delirium implemented on a smartphone platform (DelApp). We aim to evaluate the diagnostic performance of the DelApp in a representative cohort of older hospitalised patients. METHODS: This is a prospective study of older non-scheduled hospitalised patients (target n = 500, age ≥ 65), recruited from elderly care and acute orthopaedic wards. Exclusion criteria are: non-English speakers; severe vision or hearing impairment; photosensitive epilepsy. A structured reference standard delirium assessment based on DSM-5 criteria will be used, which includes a cognitive test battery administered by a trained assessor (Orientation-Memory-Concentration Test, Abbreviated Mental Test-10, Delirium Rating Severity Scale-Revised-98, digit span, months and days backwards, Vigilance A' test) and assessment of arousal (Observational Scale of Level of Arousal, Richmond Agitation Sedation Scale). Prior change in cognition will be documented using the Informant Questionnaire on Cognitive Decline in the Elderly. Patients will be categorized as delirium (with/without dementia), possible delirium, dementia, no cognitive impairment, or undetermined. A separate assessor (blinded to diagnosis and assessments) will administer the DelApp index test within 3 h of the reference standard assessment. The DelApp comprises assessment of arousal (score 0-4) and sustained attention (score 0-6), yielding a total score between 0 and 10 (higher score = better performance). Outcomes (length of stay, mortality and discharge location) will be collected at 12 weeks. We will evaluate a priori cutpoints derived from a previous case-control study. Measures of the accuracy of DelApp will include sensitivity, specificity, positive and negative predictive values, and area under the ROC curve. We plan repeat assessments on up to 4 occasions in a purposive subsample of 30 patients (15 delirium, 15 no delirium) to examine changes over time. DISCUSSION: This study evaluates the diagnostic test accuracy of a novel smartphone test for delirium in a representative cohort of older hospitalised patients, including those with dementia. DelApp has the potential to be a convenient, objective method of improving delirium assessment for older people in acute care. TRIAL REGISTRATION: Clinical trials.gov, NCT02590796 . Registered on 29 Oct 2015. Protocol version 5, dated 25 July 2016.

Pre-clinical investigations of multi-path propagation in transcranial Doppler ultrasound flow phantom.

There are various practical situations in medical applications when pre-clinical investigations must be performed using a simulation environment or test bench prior to human studies. One example is the analysis of propagation channels in Transcanial Doppler (TCD) ultrasound (US), a signal processing challenge requiring the analysis of data from US waves scattered in three dimensions (3D). When examining the effects of scatterers in such channels, it is common to use a data acquisition test bench and a Doppler flow phantom. Such medical phantoms are frequently required to verify image and signal processing systems, and are often used to support algorithm development for a wide range of imaging and blood flow assessments. In this paper we describe a TCD simulation environment for the acquisition, investigation and pre-clinical data analysis of multi-path propagation in TCD US systems. This is demonstrated by comparing the anticipated theoretical and simulation channel statistics with the measured acoustic characteristics in terms of the probability distribution and autocorrelation functions.

A wall-less poly(vinyl alcohol) cryogel flow phantom with accurate scattering properties for transcranial Doppler ultrasound propagation channels analysis.

Medical phantoms are frequently required to verify image and signal processing systems, and are often used to support algorithm development for a wide range of imaging and blood flow assessments. A phantom with accurate scattering properties is a crucial requirement when assessing the effects of multi-path propagation channels during the development of complex signal processing techniques for Transcranial Doppler (TCD) ultrasound. The simulation of physiological blood flow in a phantom with tissue and blood equivalence can be achieved using a variety of techniques. In this paper, poly (vinyl alcohol) cryogel (PVA-C) tissue mimicking material (TMM) is evaluated in conjunction with a number of potential scattering agents. The acoustic properties of the TMMs are assessed and an acoustic velocity of 1524ms(-1), an attenuation coefficient of (0:49) × 10(-4)fdBm(1)Hz(-1), a characteristic impedance of (1.72) × 10(6)Kgm(-2)s(-1) and a backscatter coefficient of (1.12) × 10(-28)f(4)m(-1)Hz(-4)sr(-1) were achieved using 4 freeze-thaw cycles and an aluminium oxide (Al(2)O(3)) scattering agent. This TMM was used to make an anatomically realistic wall-less flow phantom for studying the effects of multipath propagation in TCD ultrasound.

Development of Android apps for cognitive assessment of dementia and delirium.

The next generation of medical technology applications for hand-held portable platforms will provide a core change in performance and sophistication, transforming the way health care professionals interact with patients. This advance is particularly apparent in the delivery of cognitive patient assessments, where smartphones and tablet computers are being used to assess complex neurological conditions to provide objective, accurate and reproducible test results. This paper reports on two such applications (apps) that have been developed to assist healthcare professionals with the detection and diagnosis of dementia and delirium.