A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Introduction: Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. Methods: This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. Conclusion: The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

Optic Flow Processing in Patients With Macular Degeneration.

Purpose: Optic flow processing was characterized in patients with macular degeneration (MD). Methods: Twelve patients with dense bilateral scotomas and 12 age- and gender-matched control participants performed psychophysical experiments. Stimuli were dynamic random-dot kinematograms projected on a large screen. For each component of optic flow (translational, radial, and rotational), we estimated motion coherence discrimination thresholds in our participants using an adaptive Bayesian procedure. Results: Thresholds for translational, rotational, and radial patterns were comparable between patients and their matched control participants. A negative correlation was observed in patients between the time since MD diagnosis and coherence thresholds for translational patterns. Conclusions: Our results suggest that in patients with MD, selectivity to optic flow patterns is preserved.

Simulator Teaching of Cardiopulmonary Bypass Complications: A Prospective, Randomized Study.

OBJECTIVE: Complications of cardiopulmonary bypass (CPB) are rare, but life-threatening events that need prompt and rehearsed actions involving a team. This is not adequately taught to cardiothoracic surgical trainees. The objective of this study was to assess the knowledge of cardiothoracic trainees required to manage these events after simulation-based vs. lecture-based teaching. PARTICIPANTS AND DESIGN: Totally, 17 cardiac surgical trainees with no formal teaching in intraoperative complications of CPB management were randomly assigned by computer to either a study group receiving simulation-based complications of CPB teaching via the Orpheus simulator (n = 9) or a control group receiving complications of CPB teaching via a lecture (n = 8). Each subject undertook a written test comprising 20 multiple choice questions on complications of CPB before and after teaching. Trainees were then asked to rate their satisfaction with each session from 1 to 5, with 5 being most satisfied. SETTING: St George Simulation and Clinical Skills Laboratory, St George's Hospital, London. RESULTS: There was no significant difference in the pretest scores between the 2 groups (p = 0.29). After teaching, both groups showed a statistically significant improvement in their knowledge (p < 0.05). The trainees in the simulation group performed better than the lecture-based group; however, this was not statistically significant (p = 0.21). Satisfaction levels in both the lecture session and the simulation session were very high with means of 4.4/5 and 4.8/5, respectively. CONCLUSION: Despite the familiarity with CPB during surgery, the simulation group performed at least as well as the lecture group. Cardiothoracic trainees would benefit from formal teaching of complications of CPB management via either learning modality being incorporated into their training.

Behavior of bats at wind turbines.

Wind turbines are causing unprecedented numbers of bat fatalities. Many fatalities involve tree-roosting bats, but reasons for this higher susceptibility remain unknown. To better understand behaviors associated with risk, we monitored bats at three experimentally manipulated wind turbines in Indiana, United States, from July 29 to October 1, 2012, using thermal cameras and other methods. We observed bats on 993 occasions and saw many behaviors, including close approaches, flight loops and dives, hovering, and chases. Most bats altered course toward turbines during observation. Based on these new observations, we tested the hypotheses that wind speed and blade rotation speed influenced the way that bats interacted with turbines. We found that bats were detected more frequently at lower wind speeds and typically approached turbines on the leeward (downwind) side. The proportion of leeward approaches increased with wind speed when blades were prevented from turning, yet decreased when blades could turn. Bats were observed more frequently at turbines on moonlit nights. Taken together, these observations suggest that bats may orient toward turbines by sensing air currents and using vision, and that air turbulence caused by fast-moving blades creates conditions that are less attractive to bats passing in close proximity. Tree bats may respond to streams of air flowing downwind from trees at night while searching for roosts, conspecifics, and nocturnal insect prey that could accumulate in such flows. Fatalities of tree bats at turbines may be the consequence of behaviors that evolved to provide selective advantages when elicited by tall trees, but are now maladaptive when elicited by wind turbines.