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Amid accelerating threats to species and ecosystems, technology advancements to monitor, protect, and conserve biodiversity have taken on increased importance. While most innovations stem from adaptation of off-the-shelf devices, these tools can fail to meet the specialized needs of conservation and research or lack the support to scale beyond a single site. Despite calls from the conservation community for its importance, a shift to bottom-up innovation driven by conservation professionals remains limited. We surveyed practitioners, academic researchers, and technologists to understand the factors contributing to or inhibiting engagement in the collaborative process of technology development and adoption for field use and identify emerging technology needs. High cost was the main barrier to technology use across occupations, while development of new technologies faced barriers of cost and partner communication. Automated processing of data streams was the largest emerging need, and respondents focused mainly on applications for individual-level monitoring and automated image processing. Cross-discipline collaborations and expanded funding networks that encourage cyclical development and continued technical support are needed to address current limitations and meet the growing need for conservation technologies. [ABSTRACT FROM AUTHOR]

Objective: To rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy. Methods: Forty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer. Results: FreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant. Interpretation: Automated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur. [ABSTRACT FROM AUTHOR]

Background: Compared with the increasingly widespread use of picture archiving and communication systems (PACSs), knowledge concerning users' acceptance of such systems is limited. Knowledge of acceptance is needed given the large (and growing) financial investment associated with the implementation of PACSs, and because the level of user acceptance influences the degree to which the benefits of the systems for healthcare can be realized. Methods: A Technology Acceptance Model (TAM) was used to assess the level of acceptance of the host PACS by staff in the radiology department at King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia. A questionnaire survey of 89 PACS users was employed to obtain data regarding user characteristics, perceived usefulness (PU) (6 items), perceived ease of use (PEU) (4 items), a change construct (4 items), and a behavior (acceptance) construct (9 items). Respondents graded each item in each construct using five-point likert scales. Results: Surveyed users reported high levels of PU (4.33/5), PEU (4.15/5), change (4.26/5), and acceptance (3.86/5). The three constructs of PU, PEU, and change explained 41 % of the variation in PACS user acceptance. PU was the most important predictor, explaining 38 % of the variation on its own. The most important single item in the explanatory constructs was that users found PACS to have improved the quality of their work in providing better patient care. Technologists had lower acceptance ratings than did clinicians/radiologists, but no influence on acceptance level was found due to gender, age, or length of experience using the PACS. Although not directly measured, there appeared to be no cultural influence on either the level of acceptance or its determinants. Conclusions: User acceptance must be considered when an organization implements a PACS, in order to enhance its successful adoption. Health organizations should adopt a PACS that offers all required functions and which is likely to generate high PU on the part of its users, rather than a system that is easy to use. Training/familiarization programs should aim at establishing high levels of PU in all users, particularly technologists. Health organizations are advised to measure all the factors that influence the acceptance of a PACS by their staff, in order to optimize the productivity of the system and realize the potential benefits to the greatest extent possible. [ABSTRACT FROM AUTHOR]