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Deep convolutional neural networks (CNNs) have achieved remarkable success in various computer vision tasks. However, the lack of interpretability in these models has raised concerns and hindered their widespread adoption in critical domains. Generating activation maps that highlight the regions contributing to the CNN's decision has emerged as a popular approach to visualize and interpret these models. Nevertheless, existing methods often produce activation maps contaminated with irrelevant background noise or incomplete object activation, limiting their effectiveness in providing meaningful explanations. To address this challenge, we propose Union Class Activation Mapping (UnionCAM), an innovative visual interpretation framework that generates high-quality class activation maps (CAMs) through a novel three-step approach. UnionCAM introduces a weighted fusion strategy that adaptively combines multiple CAMs to create more informative and comprehensive activation maps. First, the denoising module removes background noise from CAMs by using adaptive thresholding. Subsequently, the union module fuses the denoised CAMs with region-based CAMs using a weighted combination scheme to obtain more comprehensive and informative maps, which we refer to as fused CAMs. Lastly, the activation map selection module automatically selects the optimal CAM that offers the best interpretation from the pool of fused CAMs. Extensive experiments on ILSVRC2012 and VOC2007 datasets demonstrate UnionCAM's superior performance over state-of-the-art methods. It effectively suppresses background noise, captures complete object regions, and provides intuitive visual explanations. UnionCAM achieves significant improvements in insertion and deletion scores, outperforming the best baseline. UnionCAM makes notable contributions by introducing a novel denoising strategy, adaptive fusion of CAMs, and an automatic selection mechanism. It bridges the gap between CNN performance and interpretability, providing a valuable tool for understanding and trusting CNN-based systems. UnionCAM has the potential to foster responsible deployment of CNNs in real-world applications., Competing Interests: HH, RW, and HY were employed by China Academy of Railway Sciences Corporation Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hu, Wang, Lin and Yu.)

Ascaridia galli is an important nematode that causes ascaridiasis in free-range and indoor system chicken farms. Infection with A. galli may damage the intestinal mucosa and inhibit nutrient absorption, leading to a reduced growth rate, weight loss and a decreased egg production. Consequently, A. galli infection is a significant health problem in chickens. In this study, we developed a loop-mediated isothermal amplification coupled with a lateral flow dipstick (LAMP-LFD) assay for the visual detection of A. galli eggs in faecal samples. The LAMP-LFD assay consists of six primers and one DNA probe that recognize the internal transcribed spacer 2 (ITS2) region; it can be performed within 70 min and the results can be interpreted with the naked eye. Using the LAMP-LFD assay developed in this study, A. galli DNA was specifically amplified without any cross-reactions with other related parasites ( Heterakis gallinarum , Raillietina echinobothrida , R. tetragona , R. cesticillus , Cotugnia sp., Echinostoma miyagawai ) and definitive hosts ( Gallus gallus domesticus , Anas platyrhynchos domesticus ). The minimum detectable DNA concentration was 5 pg/μl, and the detectable egg count was 50 eggs per reaction. The assay can be performed in a water bath, without the need for post-mortem morphological investigations and laboratory instruments. It is therefore a viable alternative for the detection of A. galli in chicken faeces and can replace classical methods in field screening for epidemiological investigations, veterinary health and poultry farming management. RESEARCH HIGHLIGHTS This is the first study using the LAMP-LFD assay for Ascaridia galli detection.The results can be observed by the naked eye.The developed assay can be used to detect Ascaridia galli eggs in faecal samples.

Purpose: Esophageal high-resolution manometry (HRM) revolutionized esophageal function testing due to the intuitive colorful and agreeable-to-the-eyes plots (Clouse plots). HRM execution and interpretation is guided by the Chicago Classification. The well-established metrics for interpretation allows a reliable automatic software analysis. Analysis based on these mathematical parameters, however, ignores the valuable visual interpretation unique to human eyes and based on expertise., Methods: We compiled some situations where visual interpretation added useful information for HRM interpretation., Results: Visual interpretation may be useful in cases of hypomotility, premature waves, artifacts, segmental abnormalities of peristalsis, and extra-luminal non-contractile findings., Conclusion: These extra findings can be reported apart from the conventional parameters., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

With the development of mobile payment, the Internet of Things (IoT) and artificial intelligence (AI), smart vending machines, as a kind of unmanned retail, are moving towards a new future. However, the scarcity of data in vending machine scenarios is not conducive to the development of its unmanned services. This paper focuses on using machine learning on small data to detect the placement of the spiral rack indicated by the end of the spiral rack, which is the most crucial factor in causing a product potentially to get stuck in vending machines during the dispensation. To this end, we propose a k-means clustering-based method for splitting small data that is unevenly distributed both in number and in features due to real-world constraints and design a remarkably lightweight convolutional neural network (CNN) as a classifier model for the benefit of real-time application. Our proposal of data splitting along with the CNN is visually interpreted to be effective in that the trained model is robust enough to be unaffected by changes in products and reaches an accuracy of 100%. We also design a single-board computer-based handheld device and implement the trained model to demonstrate the feasibility of a real-time application.

Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [ 11 C]PiB and 209 [ 18 F]AV45 PET images from ADNI database and our local cohort were used. Global mean and maximum SUVr cut-points were derived using ROC analysis. 68 ML models were built using regional SUVr values and one DL network was trained with classifications of two visual assessments - manufacturer's recommendations (gray-scale) and with visually guided reference region scaling (rainbow-scale). ML-based classification achieved similarly high accuracy as ROC classification, but had better convergence between training and unseen data, with a smaller number of training data. Naïve Bayes performed the best overall among the 68 ML algorithms. Classification with maximum SUVr cut-points yielded higher accuracy than with mean SUVr cut-points, particularly for cohorts showing more focal uptake. DL networks can support the classification of definite cases accurately but performed poorly for equivocal cases. Rainbow-scale standardized image intensity scaling and improved inter-rater agreement. Gray-scale detects focal accumulation better, thus classifying more amyloid-positive scans. All three approaches generally achieved higher accuracy when trained with rainbow-scale classification. ML yielded similarly high accuracy as ROC, but with better convergence between training and unseen data, and further work may lead to even more accurate ML methods., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

A convolutional neural network based on an improved residual structure is proposed to implement a lightweight classification model for the recognition of complex pavement conditions, which uses RGB-thermal as input and embeds an attention module to adjust the spatial, as well as channel, information of the images. The best prediction accuracy of the proposed model is 98.88%, while the RGB-thermal is used as input and an attention mechanism is used. The attention mechanism increases the attention to detail of the image and regulates the use of image channels, which enhances the final performance of the model. It is also compared with state-of-the-art (SOTA) deep learning models, indicating our model has fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models. A visualization method incorporating gradient-weighted class activation mapping (Grad-CAM) is proposed to analyze the classification results, comparing the data the model learns from the images under different input data.

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.

The proportion of the rural population who live within 2 km of an all-season road is an indicator of the United Nations' Sustainable Development Goals (SDGs) 9.1.1. This paper aims to calculate SDG indicator 9.1.1 in the proximity of five Algerian expressways. Three monitoring methods are proposed for different spatial regions based on the five expressways built by China's Belt and Road Initiative Project. These methods are based on remote sensing and WorldPop and The High Resolution Settlement Layer (HRSL) population data. The results indicate that (1) the WorldPop population statistics show that the five expressways built by China's Belt Project have increased the rural population of the 2 km buffer zone by 192,016 between the start of construction and eight years after its completion. By the end of 2019, the population increased by 329,291 accounting for 1.17% of the rural population. (2) Based on populations estimated form built-up index (NDBI) building areas, the rural populations within the 2 km buffer area of the Bejaia-Haniff Expressway in 2011, 2015, and 2019 were 273,118, 306,430, and 375,408, respectively. (3) HRSL population grid statistics indicate that, in 2015, the populations were: East-West Expressway = 911,549, Bejaia Expressway = 127,471, Tipaza Expressway = 71,411, North-South Expressway = 30,583, and Cherchell Ring Expressway = 41,657. (4) A visual interpretation method based on Google Earth imagery was used to count the number of buildings and number of building floors in the town of Tikhramtath. Based on the estimated population of each building and floor, the population of Tikhramtath town in 2011, 2015, 2017, and 2019 was estimated as 1,790, 2,785, 3,365, and 3,870, respectively. (5) Through analysis and accuracy assessment, the appropriate statistical methods for different regions were determined., Competing Interests: The authors declare there are no conflicts of interest., (©2020 Jia et al.)

Objective: The objectives of the present study were to investigate (1) whether trinary visual interpretation of amyloid positron emission tomography (PET) imaging (negative/equivocal/positive) reflects quantitative amyloid measurements and the time course of 11 C-Pittsburgh compound B (PiB) amyloid accumulation, and (2) whether visually equivocal scans represent an early stage of the Alzheimer's disease (AD) continuum in terms of an intermediate state of quantitative amyloid measurements and the changes in amyloid accumulation over time., Methods: From the National Bioscience Database Center Human Database of the Japanese Alzheimer's Disease Neuroimaging Initiative, we selected 133 individuals for this study including 33 with Alzheimer's disease dementia (ADD), 52 with late mild cognitive impairment (LMCI), and 48 cognitively normal (CN) subjects who underwent clinical assessment, PiB PET, and structural magnetic resonance imaging (MRI) with 2 or 3-years of follow-up. Sixty-eight of the 133 individuals underwent cerebrospinal fluid amyloid-β 1-42 (CSF-Ab 42 ) analysis at baseline. The standard uptake value ratio (SUVR) of PiB PET was calculated with a method using MRI at each visit. The cross-sectional values, longitudinal changes in SUVR, and baseline CSF-Ab 42 were compared among groups, which were categorized based on trinary visual reads of amyloid PET (negative/equivocal/positive)., Results: From the trinary visual interpretation of the PiB PET images, 55 subjects were negative, 8 were equivocal, and 70 were positive. Negative interpretation was most frequent in the CN group (70.8/10.4/18.8%: negative/equivocal/positive), and positive was most frequent in the LMCI group (34.6/1.9/63.5%) and in the ADD group (9.1/6.1/84.8%). The baseline SUVRs were 1.08 ± 0.06 in the negative group, 1.23 ± 0.15 in the equivocal group, and 1.86 ± 0.31 in the positive group (F = 174.9, p < 0.001). The baseline CSF-Ab 42 level was 463 ± 112 pg/mL in the negative group, 383 ± 125 pg/mL in the equivocal group, and 264 ± 69 pg/mL in the positive group (F = 37, p < 0.001). Over the 3-year follow-up, annual changes in SUVR were - 0.00 ± 0.02 in the negative group, 0.02 ± 0.02 in the equivocal group, and 0.04 ± 0.07 in the positive group (F = 8.4, p < 0.001)., Conclusions: Trinary visual interpretation (negative/equivocal/positive) of amyloid PET imaging reflects quantitative amyloid measurements evaluated with PET and the CSF amyloid test as well as the amyloid accumulation over time evaluated with PET over 3 years. Subjects in the early stage of the AD continuum could be identified with an equivocal scan, because they showed intermediate quantitative amyloid PET, CSF measurements, and the amyloid accumulation over time.

Traditional Chinese medicine is planted in mountainous areas with suitable natural conditions. The planting area is complex in terrain,and the planting plots are mostly irregularly shaped. It is difficult to accurately calculate the planting area by traditional survey methods. The method of extracting Chinese herbal medicine planting area combined with remote sensing and GIS technology is of great significance for the rational development and utilization of traditional Chinese medicine resources. Taking Bletilla striata planting in Ningshan county of Shaanxi province as an example,the extraction method of planting area of traditional Chinese medicine in county was studied. High-resolution ZY-3 and GF-1 multi-spectral multi-temporal remote sensing images were used as data sources. Through field sampling,samples such as B. striata,cultivated land,forest land,water body,artificial surface,alpine meadow,etc. are collected. The spectral features,texture features and shape features of remotely identifiable objects in different planting areas and cultivated land,vegetable sheds were analyzed,confusing ground objects were eliminated and interpretation marks were establish. The method of visual interpretation is used to realize the extraction of B. striata planting areas,and the B. striata planting area are calculated by combining GIS technology. The results showed that the method of visual interpretation,using high-resolution ZY-3 and GF-1 multi-spectral multi-temporal remote sensing image data extracted the planting area of 403.05 mu. It can effectively extract the B. striata planting area in research region.

Due to the large amount of nutrients required during the cultivation of Angelica sinensis and in order to prevent the occurrence of pests and diseases,and the annual reduction of the planting area of Angelica and the balance of supply and demand of A. sinensis,the A. sinensis plantation adopts the rotation mode. This paper takes Wuyuan county of Gansu province as the research scope and use GF-1 Satellite data as the data source,using remote sensing technology combined with field survey results,to explore the effective method of visual interpretation for the extraction of A. sinensis planting area. A sample was selected to generate a spectrum according to different feature types. The different characteristics of A. sinensis and other features were analyzed and distinguished in remote sensing images,so that the A. sinensis planting plots were extracted and verified in remote sensing images. The results showed that the accuracy verification value of the visual interpretation method was 95. 85%. It is determined that the visual interpretation method can effectively extract the A. sinensis planting plots within the research scope and realize the comprehensive grasp of the spatial distribution information of A. sinensis.

Gully erosion is a serious environmental problem worldwide, causing soil loss, land degradation, silting up of reservoirs and even catastrophic flooding. Mapping gully features from remote sensing imagery is crucial for assisting in understanding gully erosion mechanisms, predicting its development processes and assessing its environmental and socio-economic effects over large areas, especially under the increasing global climate extremes and intensive human activities. However, the potential of using increasingly available high-resolution remote sensing imagery to detect and delineate gullies has been less evaluated. Hence, 130 gullies occurred along a transect were selected from a typical watershed in the hilly and gully region of the Chinese Loess Plateau, and visually interpreted from a Pleiades-1B satellite image (panchromatic-sharpened image at 0.5 m resolution fused with 2.0 m multi-spectral bands). The interpreted gullies were compared with their measured data obtained in the field using a differential global positioning system (GPS). Results showed that gullies could generally be accurately interpreted from the image, with an average relative error of gully area and gully perimeter being 11.1% and 8.9%, respectively, and 74.2% and 82.3% of the relative errors for gully area and gully perimeter were within 15%. But involving field measurements of gullies in present imagery-based gully studies is still recommended. To judge whether gullies were mapped accurately further, a standard adopting one-pixel tolerance along the mapped gully edges was proposed and proved to be practical. Correlation analysis indicated that larger gullies could be interpreted more accurately but increasing gully shape complexity would decrease interpreting accuracy. Overall lower vegetation coverage in winter due to the withering and falling of vegetation rarely affected gully interpreting. Furthermore, gully detectability on remote sensing imagery in this region was lower than the other places of the world, due to the overall broken topography in the Loess Plateau, thus images with higher resolution than normally perceived are needed when mapping erosion features here. Taking these influencing factors (gully dimension and shape complexity, vegetation coverage, topography) into account will be favorable to select appropriate imagery and gullies (as study objects) in future imagery-based gully studies. Finally, two linear regression models were built to correct gully area (A ip , m²) and gully perimeter ( P ip , m) visually extracted, by connecting them with the measured area ( A ms , m²) and perimeter ( P ms , m). The correction models were Ams=1.021Aip+0.139 and Pms=0.949Pip+ 0.722, respectively. These models could be helpful for improving the accuracy of interpreting results, and further accurately estimating gully development and developing more effective automated gully extraction methods on the Loess Plateau of China., Competing Interests: The authors declare no conflict of interest.

Environmental monitoring and assessment of the extent and change of land uses and their renewable natural resources over time is a key element in many international processes and one crucial basis for sustainable management. Remote sensing plays an increasingly important role in these monitoring systems, especially if the interest is in large areas. Integration of remote sensing requires comprehensive and careful preprocessing and a high level of expertise which is not always at hand in all applications. However, easy-to-implement sampling techniques based on visual interpretation are an alternative approach for utilizing remote sensing imagery, including the evolving archives of georeferenced and preprocessed data provided by virtual globes like Google Earth, Bing, and others. The goal of this paper is to propose a simple unified framework that may be used in the context of sampling studies and environmental monitoring from local to global scale. Besides the definition of a sampling design, the observation or plot design, i.e., defining how observations are to be made and recorded, has a strong influence on the precision of estimates as well as the overall efficiency of a sampling exercise. As an example, we present a simulation study focusing on the estimation of forest cover in artificial landscapes with different coverage and degree of fragmentation. The sampling units we compare are point clusters with different configuration and spatial extent.

Background: The indirect immunofluorescence assay (IIFA) for the detection of antinuclear antibodies (ANA) was firstly described in 1958 and is still considered the reference method for ANA screening. Currently, an automated processing and recognition system for standardized and efficient ANA interpretation by human epithelial (HEp-2) cell-based immunofluorescence (IIF; EUROPattern Suite, Euroimmun) is available in China., Methods: In this study, the performance of this novel system for positive/negative classification, pattern recognition (including homogenous, speckled, nucleolar, nuclear dots, cytoplasmic, and centromeres patterns) and titers evaluation was evaluated by comparing to visual interpretation., Results: Referring to the total of 3681 collected samples, there was an agreement of 98.7% (κ = 0.973) between the visual and automated examination regarding positive/negative discrimination. In sera with single pattern, correct pattern recognition was observed in 94.6% of the samples. The efficiency of automated recognition for single pattern varied for the different patterns. The automatically determined patterns were correct and complete in 1071 of 1620 cases and correct and meaningful but not complete ("main pattern") in another 405 cases, enabling main pattern recognition in 91.1% of all cases. Referring to the titers evaluation, the results within the next titer were considered to be consistent. In 1603 positive sera both by visual and automated evaluation, titers of 1514 sample were consistent, accounting for 94.4%., Conclusion: Attributed to the performance characteristics, EUROPattern system is suitable for clinical use as its high degree of automation and result reliability, and may help clinical laboratories to standardize of IIF evaluation., (© 2018 Wiley Periodicals, Inc.)

There is an increasing evidence that smallholder farms contribute substantially to food production globally, yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used. However, both have limitations, for example, automatic field size delineation using remote sensing has not yet been implemented at a global scale while the spatial resolution is very coarse when using census data. This paper demonstrates a unique approach to quantifying and mapping agricultural field size globally using crowdsourcing. A campaign was run in June 2017, where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130 K unique locations around the globe. Using this sample, we have produced the most accurate global field size map to date and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental, and national levels. The results show that smallholder farms occupy up to 40% of agricultural areas globally, which means that, potentially, there are many more smallholder farms in comparison with the two different current global estimates of 12% and 24%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modeling, comparative studies of agricultural dynamics across different contexts, for training and validation of remote sensing field size delineation, and potential contributions to the Sustainable Development Goal of Ending hunger, achieve food security and improved nutrition and promote sustainable agriculture., (© 2018 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.)

Computational models of vision have advanced in recent years at a rapid rate, rivalling in some areas human-level performance. Much of the progress to date has focused on analysing the visual scene at the object level-the recognition and localization of objects in the scene. Human understanding of images reaches a richer and deeper image understanding both 'below' the object level, such as identifying and localizing object parts and sub-parts, as well as 'above' the object level, such as identifying object relations, and agents with their actions and interactions. In both cases, understanding depends on recovering meaningful structures in the image, and their components, properties and inter-relations, a process referred here as 'image interpretation'. In this paper, we describe recent directions, based on human and computer vision studies, towards human-like image interpretation, beyond the reach of current schemes, both below the object level, as well as some aspects of image interpretation at the level of meaningful configurations beyond the recognition of individual objects, and in particular, interactions between two people in close contact. In both cases the recognition process depends on the detailed interpretation of so-called 'minimal images', and at both levels recognition depends on combining 'bottom-up' processing, proceeding from low to higher levels of a processing hierarchy, together with 'top-down' processing, proceeding from high to lower levels stages of visual analysis., Competing Interests: We declare we have no competing interests.

In this study, we used bands 7, 4, and 3 of the Advance Himawari Imager (AHI) data, combined with a Threshold Algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred on the China-Mongolia border regions, between 05:40 (UTC) on April 19th to 13:50 (UTC) on April 21st 2016. The results of the AHI data monitoring are evaluated by the fire point product data, the wind field data, and the environmental information data of the area in which the fire took place. The monitoring result shows that, the grassland fire burned for two days and eight hours with a total burned area of about 2708.29 km². It mainly spread from the northwest to the southeast, with a maximum burning speed of 20.9 m/s, a minimum speed of 2.52 m/s, and an average speed of about 12.07 m/s. Thus, using AHI data can not only quickly and accurately track the dynamic development of a grassland fire, but also estimate the spread speed and direction. The evaluation of fire monitoring results reveals that AHI data with high precision and timeliness can be highly consistent with the actual situation., Competing Interests: The authors declare no conflict of interest.

Objective: The objective of the study was to evaluate the prognostic value of positron emission tomography (PET)/computed tomography (CT) visual interpretation in patients with aggressive non-Hodgkin's lymphoma (NHL) using a meta-analysis and systematic review., Methods: Using the PubMed, Embase, and Web of Science databases, we performed a systematic review of the use of visual evaluation mid-chemotherapy to evaluate the prognosis of aggressive NHL in studies published up to May 2017. Prospective and retrospective studies assessing progression-free survival (PFS) and overall survival (OS) were included. We used hazard ratio (HR) to determine the value of Deauville criteria and International Harmonization Project (IHP) criteria for measuring survival. Subgroup analysis was performed based on the number of chemotherapy cycles before the mid-term evaluation as well as the visual evaluation method., Results: A total of 11 studies were included. PFS (HR =2.93, 95% confidence interval [CI]: 2.93-3.90, p <0.0001) and OS (HR =2.55, 95% CI: 1.76-3.68, p <0.0001) of PET/CT-positive patients were significantly lower when determined by the visual method. In subgroup analysis, IHP, Deauville criteria, and having no standard interpretation groups were factors able to predict PFS; IHP and having no standard interpretation group were able to predict OS. With PET/CT, IHP, and Deauville 5-point criteria, the PFS of patients receiving 2-4 cycles of chemotherapy before PET/CT was significantly lower than that of PET/CT-negative patients. No significant difference in OS was observed when patients received 3 or fewer cycles of chemotherapy before PET/CT, though OS was significantly lower in patients receiving more than 3 chemotherapy cycles., Conclusion: IHP and Deauville criteria are commonly used for PET/CT visual evaluation at present. Interim PET/CT analysis after 3-4 chemotherapy cycles is capable of predicting disease prognosis. Large-scale prospective clinical trials are needed to confirm whether PET/CT analysis can be used as an indication for changing a treatment strategy., Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Visual interpretation of 123 I-ioflupane SPECT images has high diagnostic accuracy for differentiating parkinsonian syndromes (PS), from essential tremor and probable dementia with Lewy bodies (DLB) from Alzheimer disease. In this study, we investigated the impact on accuracy and reader confidence offered by the addition of image quantification in comparison with visual interpretation alone. Methods: We collected 304 123 I-ioflupane images from 3 trials that included subjects with a clinical diagnosis of PS, non-PS (mainly essential tremor), probable DLB, and non-DLB (mainly Alzheimer disease). Images were reconstructed with standardized parameters before striatal binding ratios were quantified against a normal database. Images were assessed by 5 nuclear medicine physicians who had limited prior experience with 123 I-ioflupane interpretation. In 2 readings at least 1 mo apart, readers performed either a visual interpretation alone or a combined reading (i.e., visual plus quantitative data were available). Readers were asked to rate their confidence of image interpretation and judge scans as easy or difficult to read. Diagnostic accuracy was assessed by comparing image results with the standard of truth (i.e., diagnosis at follow-up) by measuring the positive percentage of agreement (equivalent to sensitivity) and the negative percentage of agreement (equivalent to specificity). The hypothesis that the results of the combined reading were not inferior to the results of the visual reading analysis was tested. Results: A comparison of the combined reading and the visual reading revealed a small, insignificant increase in the mean negative percentage of agreement (89.9% vs. 87.9%) and equivalent positive percentages of agreement (80.2% vs. 80.1%). Readers who initially performed a combined analysis had significantly greater accuracy (85.8% vs. 79.2%; P = 0.018), and their accuracy was close to that of the expert readers in the original studies (range, 83.3%-87.2%). Mean reader confidence in the interpretation of images showed a significant improvement when combined analysis was used ( P < 0.0001). Conclusion: The addition of quantification allowed readers with limited experience in the interpretation of 123 I-ioflupane SPECT scans to have diagnostic accuracy equivalent to that of the experienced readers in the initial studies. Also, the results of the combined reading were not inferior to the results of the visual reading analysis and offered an increase in reader confidence., (© 2017 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: To investigate a method for optimizing the display conditions of brain magnetic resonance (MR) images., Materials and Methods: We retrospectively analyzed brain MR images of 120 adults classified into screening, acute cerebral infarction, and brain tumor groups (n = 40 each). Two observers independently displayed the images on a monitor and optimized the display conditions using the W/L and U/L methods. In the W/L method, the observers manipulated the width and level of the display window, while in the U/L method they manipulated the upper and lower levels of the window. The times required were compared between the two methods. Additionally, the appropriateness of the determined window setting was evaluated visually by the respective observer to exclude the possibility that rough, suboptimal adjustment shortened the adjustment time., Results: For both observers and all groups, the time required for optimization was significantly shorter for the U/L method than for the W/L method. The appropriateness of the window setting for the U/L method was equal to or better than that for the W/L method., Conclusion: Manipulating the upper and lower levels of the display window appears to improve the efficiency of interpreting brain MR images through rapid optimization of the display condition.