Computer-aided assessment of diagnostic images for epidemiological research

<p>Abstract</p> <p>Background</p> <p>Diagnostic images are often assessed for clinical outcomes using subjective methods, which are limited by the skill of the reviewer. Computer-aided diagnosis (CAD) algorithms that assist reviewers in their decisions concerning outcomes have been developed to increase sensitivity and specificity in the clinical setting. However, these systems have not been well utilized in research settings to improve the measurement of clinical endpoints. Reductions in bias through their use could have important implications for etiologic research.</p> <p>Methods</p> <p>Using the example of cortical cataract detection, we developed an algorithm for assisting a reviewer in evaluating digital images for the presence and severity of lesions. Available image processing and statistical methods that were easily implementable were used as the basis for the CAD algorithm. The performance of the system was compared to the subjective assessment of five reviewers using 60 simulated images. Cortical cataract severity scores from 0 to 16 were assigned to the images by the reviewers and the CAD system, with each image assessed twice to obtain a measure of variability. Image characteristics that affected reviewer bias were also assessed by systematically varying the appearance of the simulated images.</p> <p>Results</p> <p>The algorithm yielded severity scores with smaller bias on images where cataract severity was mild to moderate (approximately ≤ 6/16^<it>ths</it>). On high severity images, the bias of the CAD system exceeded that of the reviewers. The variability of the CAD system was zero on repeated images but ranged from 0.48 to 1.22 for the reviewers. The direction and magnitude of the bias exhibited by the reviewers was a function of the number of cataract opacities, the shape and the contrast of the lesions in the simulated images.</p> <p>Conclusion</p> <p>CAD systems are feasible to implement with available software and can be valuable when medical images contain exposure or outcome information for etiologic research. Our results indicate that such systems have the potential to decrease bias and discriminate very small changes in disease severity. Simulated images are a tool that can be used to assess performance of a CAD system when a gold standard is not available.</p

Quantifying the learning curve for pulmonary thromboendarterectomy

Abstract Background Pulmonary thromboendarterectomy (PTE) is an effective treatment for chronic thromboembolic pulmonary hypertension (CTEPH), but is a technically challenging operation for cardiothoracic surgeons. Starting a new program allows an opportunity to define a learning curve for PTE. Methods A retrospective case review was performed of 134 consecutive PTEs performed from 1998 to 2016 at a single institution. Outcomes were compared using either a two-tailed t-test for continuous variables or a chi-squared test for categorical variables according to experience of the program by terciles (T). Results The 30-day mortality was 3.7%. The mean length of hospital stay, length of ICU stay, and duration on a ventilator were 12.6 days, 4.6 days, and 2.0 days, respectively. The mean decrease in systolic pulmonary artery pressure (sPAP) was 41.3 mmHg. Patients with Jamieson type 2 disease had a greater change in mean sPAP than those with type 3 disease (p = 0.039). The mean cardiopulmonary bypass time was 180 min (T1–198 min, T3–159 min, p = <0.001), and the mean circulatory arrest time was 37 min (T1-44 min, T3-31 min, p < 0.001). Plotting circulatory arrest times as a running sum compared to the mean demonstrated 2 inflection points, the first at 22 cases and the second at 95 cases. Conclusions PTE is a challenging procedure to learn, and good outcomes are a result of a multi-disciplinary effort to optimize case selection, operative performance, and postoperative care. Approximately 20 cases are needed to become proficient in PTE, and nearly 100 cases are required for more efficient clearing of obstructed pulmonary arteries

Minireview: the neuroendocrine regulation of puberty: is the time ripe for a system biology approach?

The initiation of mammalian puberty requires an increase in pulsatile release of GnRH from the hypothalamus. This increase is brought about by coordinated changes in transsynaptic and glial-neuronal communication. As the neuronal and glial excitatory inputs to the GnRH neuronal network increase, the transsynaptic inhibitory tone decreases, leading to the pubertal activation of GnRH secretion. The excitatory neuronal systems most prevalently involved in this process use glutamate and the peptide kisspeptin for neurotransmission/ neuromodulation, whereas the most important inhibitory inputs are provided by γ-aminobutyric acid (GABA)ergic and opiatergic neurons. Glial cells, on the other hand, facilitate GnRH secretion via growth factor-dependent cell-cell signaling. Coordination of this regulatory neuronal-glial network may require a hierarchical arrangement. One level of coordination appears to be provided by a host of unrelated genes encoding proteins required for cell-cell communication. A second, but overlapping, level might be provided by a second tier of genes engaged in specific cell functions required for productive cell-cell interaction. A third and higher level of control involves the transcriptional regulation of these subordinate genes by a handful of upper echelon genes that, operating within the different neuronal and glial subsets required for the initiation of the pubertal process, sustain the functional integration of the network. The existence of functionally connected genes controlling the pubertal process is consistent with the concept that puberty is under genetic control and that the genetic underpinnings of both normal and deranged puberty are polygenic rather than specified by a single gene. The availability of improved high-throughput techniques and computational methods for global analysis of mRNAs and proteins will allow us to not only initiate the systematic identification of the different components of this neuroendocrine network but also to define their functional interactions