Beachgoers' ability to identify rip currents at a beach in situ

Rip currents (“rips”) are the leading cause of drowning on surf beaches worldwide. A major contributing factor is that many beachgoers are unable to identify rip currents. Previous research has attempted to quantify beachgoers' rip identification ability using photographs of rip currents without identifying whether this usefully translates into an ability to identify a rip current in situ at the beach. This study is the first to compare beachgoers ability to identify rip currents in photographs and in situ at a beach in New Zealand (Muriwai Beach) where a channel rip current was present. Only 22 % of respondents were able to identify the in situ rip current. The highest rates of success were for males (33 %), New Zealand residents (25 %), and local beach users (29 %). Of all respondents who were successful at identifying the rip current in situ, 62 % were active surfers/bodyboarders, and 28 % were active beach swimmers. Of the respondents who were able to identify a rip current in two photographs, only 34 % were able to translate this into a successful in situ rip identification, which suggests that the ability to identify rip currents by beachgoers is worse than reported by previous studies involving photographs. This study highlights the difficulty of successfully identifying a rip current in reality and that photographs are not necessarily a useful means of teaching individuals to identify rip currents. It advocates for the use of more immersive and realistic education strategies, such as the use of virtual reality headsets showing moving imagery (videos) of rip currents in order to improve rip identification ability

The impact of immediate breast reconstruction on the time to delivery of adjuvant therapy: the iBRA-2 study

Background: Immediate breast reconstruction (IBR) is routinely offered to improve quality-of-life for women requiring mastectomy, but there are concerns that more complex surgery may delay adjuvant oncological treatments and compromise long-term outcomes. High-quality evidence is lacking. The iBRA-2 study aimed to investigate the impact of IBR on time to adjuvant therapy. Methods: Consecutive women undergoing mastectomy ± IBR for breast cancer July–December, 2016 were included. Patient demographics, operative, oncological and complication data were collected. Time from last definitive cancer surgery to first adjuvant treatment for patients undergoing mastectomy ± IBR were compared and risk factors associated with delays explored. Results: A total of 2540 patients were recruited from 76 centres; 1008 (39.7%) underwent IBR (implant-only [n = 675, 26.6%]; pedicled flaps [n = 105,4.1%] and free-flaps [n = 228, 8.9%]). Complications requiring re-admission or re-operation were significantly more common in patients undergoing IBR than those receiving mastectomy. Adjuvant chemotherapy or radiotherapy was required by 1235 (48.6%) patients. No clinically significant differences were seen in time to adjuvant therapy between patient groups but major complications irrespective of surgery received were significantly associated with treatment delays. Conclusions: IBR does not result in clinically significant delays to adjuvant therapy, but post-operative complications are associated with treatment delays. Strategies to minimise complications, including careful patient selection, are required to improve outcomes for patients

Towards the automatic detection of pre-existing termite mounds through UAS and hyperspectral imagery

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms' outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is ''resolution-dependent''. These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only

Interpretable Deep Learning Applied to Rip Current Detection and Localization

A rip current is a strong, localized current of water which moves along and away from the shore. Recent studies have suggested that drownings due to rip currents are still a major threat to beach safety. Identification of rip currents is important for lifeguards when making decisions on where to designate patrolled areas. The public also require information while deciding where to swim when lifeguards are not on patrol. In the present study we present an artificial intelligence (AI) algorithm that both identifies whether a rip current exists in images/video, and also localizes where that rip current occurs. While there have been some significant advances in AI for rip current detection and localization, there is a lack of research ensuring that an AI algorithm can generalize well to a diverse range of coastal environments and marine conditions. The present study made use of an interpretable AI method, gradient-weighted class-activation maps (Grad-CAM), which is a novel approach for amorphous rip current detection. The training data/images were diverse and encompass rip currents in a wide variety of environmental settings, ensuring model generalization. An open-access aerial catalogue of rip currents were used for model training. Here, the aerial imagery was also augmented by applying a wide variety of randomized image transformations (e.g., perspective, rotational transforms, and additive noise), which dramatically improves model performance through generalization. To account for diverse environmental settings, a synthetically generated training set, containing fog, shadows, and rain, was also added to the rip current images, thus increased the training dataset approximately 10-fold. Interpretable AI has dramatically improved the accuracy of unbounded rip current detection, which can correctly classify and localize rip currents about 89% of the time when validated on independent videos from surf-cameras at oblique angles. The novelty also lies in the ability to capture some shape characteristics of the amorphous rip current structure without the need of a predefined bounding box, therefore enabling the use of remote technology like drones. A comparison with well-established coastal image processing techniques is also presented via a short discussion and easy reference table. The strengths and weaknesses of both methods are highlighted and discussed

Therapeutic mammaplasty is a safe and effective alternative to mastectomy with or without immediate breast reconstruction

Background: Therapeutic mammaplasty (TM) may be an alternative to mastectomy, but few well designed studies have evaluated the success of this approach or compared the short-term outcomes of TM with mastectomy with or without immediate breast reconstruction (IBR). Data from the national iBRA-2 and TeaM studies were combined to compare the safety and short-term outcomes of TM and mastectomy with or without IBR. Methods: The subgroup of patients in the TeaM study who underwent TM to avoid mastectomy were identified, and data on demographics, complications, oncology and adjuvant treatment were compared with those of patients undergoing mastectomy with or without IBR in the iBRA-2 study. The primary outcome was the percentage of successful breast-conserving procedures in the TM group. Secondary outcomes included postoperative complications and time to adjuvant therapy. Results: A total of 2916 patients (TM 376; mastectomy 1532; mastectomy and IBR 1008) were included in the analysis. Patients undergoing TM were more likely to be obese and to have undergone bilateral surgery than those having IBR. However, patients undergoing mastectomy with or without IBR were more likely to experience complications than the TM group (TM: 79, 21·0 per cent; mastectomy: 570, 37·2 per cent; mastectomy and IBR: 359, 35·6 per cent; P < 0·001). Breast conservation was possible in 87·0 per cent of patients who had TM, and TM did not delay adjuvant treatment. Conclusion: TM may allow high-risk patients who would not be candidates for IBR to avoid mastectomy safely. Further work is needed to explore the comparative patient-reported and cosmetic outcomes of the different approaches, and to establish long-term oncological safety