Does early mentorship in child and adolescent psychiatry make a difference? The Klingenstein Third-Generation Foundation Medical Student Fellowship Program.

OBJECTIVE: There is a critical shortage of child and adolescent psychiatrists in the United States. Increased exposure, through mentorship, clinical experiences, and research opportunities, may increase the number of medical students selecting child and adolescent psychiatry (CAP) as a career choice. METHOD: Between 2008 and 2011, 241 first-year participants of a program to increase exposure to CAP, funded by the Klingenstein Third-Generation Foundation (KTGF) at 10 medical schools completed baseline surveys assessing their opinions of and experiences in CAP, and 115 second-year participants completed follow-up surveys to reflect 1 year of experience in the KTGF Program. RESULTS: Students reported significantly increased positive perception of mentorship for career and research guidance, along with perceived increased knowledge and understanding of CAP. CONCLUSIONS: Results suggest that the KTGF Program positively influenced participating medical students, although future studies are needed to determine whether these changes will translate into more medical students entering the field of CAP.

Foibles, follies, and fusion: web-based collaboration for medical image labeling.

Labels that identify specific anatomical and functional structures within medical images are essential to the characterization of the relationship between structure and function in many scientific and clinical studies. Automated methods that allow for high throughput have not yet been developed for all anatomical targets or validated for exceptional anatomies, and manual labeling remains the gold standard in many cases. However, manual placement of labels within a large image volume such as that obtained using magnetic resonance imaging (MRI) is exceptionally challenging, resource intensive, and fraught with intra- and inter-rater variability. The use of statistical methods to combine labels produced by multiple raters has grown significantly in popularity, in part, because it is thought that by estimating and accounting for rater reliability estimates of the true labels will be more accurate. This paper demonstrates the performance of a class of these statistical label combination methodologies using real-world data contributed by minimally trained human raters. The consistency of the statistical estimates, the accuracy compared to the individual observations, and the variability of both the estimates and the individual observations with respect to the number of labels are presented. It is demonstrated that statistical fusion successfully combines label information using data from online (Internet-based) collaborations among minimally trained raters. This first successful demonstration of a statistically based approach using minimally trained raters opens numerous possibilities for very large scale efforts in collaboration. Extension and generalization of these technologies for new applications will certainly present fascinating areas for continuing research.

Characterizing and Optimizing Rater Performance for Internet-based Collaborative Labeling.

Labeling structures on medical images is crucial in determining clinically relevant correlations with morphometric and volumetric features. For the exploration of new structures and new imaging modalities, validated automated methods do not yet exist, and so researchers must rely on manually drawn landmarks. Voxel-by-voxel labeling can be extremely resource intensive, so large-scale studies are problematic. Recently, statistical approaches and software have been proposed to enable Internet-based collaborative labeling of medical images. While numerous labeling software tools have been created, the use of these packages as high-throughput labeling systems has yet to become entirely viable given training requirements. Herein, we explore two modifications to a typical mouse-based labeling system: (1) a platform independent overlay for recognition of mouse gestures and (2) an inexpensive touch-screen tracking device for non-mouse input. Through this study we characterize rater reliability in point, line, curve, and region placement. For the mouse input, we find a placement accuracy of 2.48±5.29 pixels (point), 0.630±1.81 pixels (curve), 1.234±6.99 pixels (line), and 0.058±0.027 (1 - Jaccard Index for region). The gesture software increased labeling speed by 27% overall and accuracy by approximately 30-50% on point and line tracing tasks, but the touch screen module lead to slower and more error prone labeling on all tasks, likely due to relatively poor sensitivity. In summary, the mouse gesture integration layer runs as a seamless operating system overlay and could potentially benefit any labeling software; yet, the inexpensive touch screen system requires improved usability optimization and calibration before it can provide an efficient labeling system.