The sustainability impacts of a web-based outpatient booking application.

Background: Climate change is a critical global issue, impacting ecosystems, economies and communities worldwide. The shift from paper-based to digital systems is becoming increasingly prevalent across industries, with downstream positive impacts on sustainability. In 2020, Austin Health, a public tertiary hospital in Victoria, Australia, adopted a web-based outpatient booking application, which succeeded the prior paper-based system.

Verification of a simplified aneurysm dimensionless flow parameter to predict intracranial aneurysm rupture status.

Objectives: Aneurysm number (An) is a novel prediction tool utilizing parameters of pulsatility index (PI) and aneurysm geometry. An has been shown to have the potential to differentiate intracranial aneurysm (IA) rupture status. The objective of this study is to investigate the feasibility and accuracy of An for IA rupture status prediction using Australian based clinical data.

Anterolateral Thigh Flap Reconstruction of Full Thickness Lateral Abdominal Wall Defect from Desmoid Tumour.

Desmoid tumours are benign but locally aggressive mesenchymal neoplasms that occur most commonly in the abdomen, with the potential to invade surrounding structures causing significant morbidity. Lateral abdominal wall defects are known to be more challenging and less frequently encountered compared to ventral abdominal wall defects. Asymmetric forces caused by contraction of remnant rectus and contralateral oblique muscles increase the risk of herniation postoperatively.

Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging.

Background: Deep learning (DL) algorithms are playing an increasing role in automatic medical image analysis.

Purpose: To evaluate the performance of a DL model for the automatic detection of intracranial haemorrhage and its subtypes on non-contrast CT (NCCT) head studies and to compare the effects of various preprocessing and model design implementations.

Methods: The DL algorithm was trained and externally validated on open-source, multi-centre retrospective data containing radiologist-annotated NCCT head studies.

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study.

Background: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across many different locations. In this proof-of-concept paper we (a) examine whether WiFi fingerprinting is a feasible machine learning (ML)-based real-time location system (RTLS) technology that can provide accurate real-time location information within a hospital setting, and (b) hypothesize its potential application in streamlining acute stroke endovascular intervention.

Methods: We conducted our study in a comprehensive stroke care unit in Melbourne, Australia that offers a 24-hour mechanical thrombectomy service.

Cleft Nasal Stent Production Using Three-Dimensional Scanning and Printing Technology.

In most cleft centers worldwide, nasal stents are routinely used in the postoperative period to prevent collapse of the lower lateral cartilage and maintain the shape of the nostrils as well as nasal alar. Prefabricated nasal stents are expensive and do not offer options for customization. In this paper, we introduce a cost-effective technique for manufacturing nasal stents using three-dimensional scanning and printing technology.

Artificial intelligence in clinical decision support and outcome prediction - applications in stroke.

Artificial intelligence (AI) is making a profound impact in healthcare, with the number of AI applications in medicine increasing substantially over the past five years. In acute stroke, it is playing an increasingly important role in clinical decision-making. Contemporary advances have increased the amount of information - both clinical and radiological - which clinicians must consider when managing patients.

Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging.

Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head.

Propensity score-matched analysis of early outcomes after laparoscopic-assisted versus open pancreaticoduodenectomy.

Background: Minimally invasive pancreaticoduodenectomy (PD) is a feasible option for periampullary tumours. However, it remains a complex procedure with no proven advantages over open PD (OPD). The aim of the study was to compare the outcomes between laparoscopic-assisted PD (LAPD) and OPD using a propensity score-matched analysis.