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

ABSTRACT: 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.

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. We report a case of a challenging abdominal wall reconstruction after desmoid tumour resection in a 62-year-old male patient who presented to our hospital with a progressively enlarging left upper back lump of 6 months duration. A venous supercharged pedicled anterolateral thigh flap was combined with PROLENE® mesh for reconstruction, and the patient recovered well with good functional and aesthetic outcomes at 2-year follow-up. The pedicled anterolateral thigh flap with venous supercharging can be effectively used for the reconstruction of extensive lateral abdominal wall defects.

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. METHODS: A retrospective study was conducted across three tertiary referral hospitals between November 2017 and November 2020 and all saccular IAs with known rupture status were included. Two sets of An values were calculated based on two sets of PI values previously reported in the literature. RESULTS: Five hundred and four IA cases were included in this study. The results demonstrated no significant difference between ruptured and unruptured status when using An ≥1 as the discriminator. Further analysis showed no strong correlation between An and IA subtypes. The area under the curve (AUC) indicated poor performance in predicting rupture status (AUC1 = 0.55 and AUC2 = 0.56). CONCLUSIONS: This study does not support An ≥1 as a reliable parameter to predict the rupture status of IAs based on a retrospective cohort. Although the concept of An is supported by hemodynamic aneurysm theory, further research is needed before it can be applied in the clinical setting. ADVANCES IN KNOWLEDGE: This study demonstrates that the novel prediction tool, An, proposed in 2020 is not reliable and that further research of this hemodynamic model is needed before it can be incorporated into the prediction of IA rupture status.

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. The training dataset was sourced from four research institutions across Canada, the USA and Brazil. The test dataset was sourced from a research centre in India. A convolutional neural network (CNN) was used, with its performance compared against similar models with additional implementations: (1) a recurrent neural network (RNN) attached to the CNN, (2) preprocessed CT image-windowed inputs and (3) preprocessed CT image-concatenated inputs. The area under the receiver operating characteristic curve (AUC-ROC) and microaveraged precision (mAP) score were used to evaluate and compare model performances. RESULTS: The training and test datasets contained 21,744 and 491 NCCT head studies, respectively, with 8,882 (40.8%) and 205 (41.8%) positive for intracranial haemorrhage. Implementation of preprocessing techniques and the CNN-RNN framework increased mAP from 0.77 to 0.93 and increased AUC-ROC [95% confidence intervals] from 0.854 [0.816-0.889] to 0.966 [0.951-0.980] (p-value = 3.91 × 10-12). CONCLUSIONS: The deep learning model accurately detected intracranial haemorrhage and improved in performance following specific implementation techniques, demonstrating clinical potential as a decision support tool and an automated system to improve radiologist workflow efficiency. KEY POINTS: • The deep learning model detected intracranial haemorrhages on computed tomography with high accuracy. • Image preprocessing, such as windowing, plays a large role in improving deep learning model performance. • Implementations which enable an analysis of interslice dependencies can improve deep learning model performance. • Visual saliency maps can facilitate explainable artificial intelligence systems. • Deep learning within a triage system may expedite earlier intracranial haemorrhage detection.

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. ML algorithms including K-nearest neighbors, decision tree, random forest, support vector machine and ensemble models were trained and tested on a public WiFi dataset and the study hospital WiFi dataset. The hospital dataset was collected using the WiFi explorer software (version 3.0.2) on a MacBook Pro (AirPort Extreme, Broadcom BCM43x×1.0). Data analysis was implemented in the Python programming environment using the scikit-learn package. The primary statistical measure for algorithm performance was the accuracy of location prediction. RESULTS: ML-based WiFi fingerprinting can accurately predict the different hospital zones relevant in the acute endovascular intervention workflow such as emergency department, CT room and angiography suite. The most accurate algorithms were random forest and support vector machine, both of which were 98% accurate. The algorithms remain robust when new data points, which were distinct from the training dataset, were tested. CONCLUSIONS: ML-based RTLS technology using WiFi fingerprinting has the potential to streamline delivery of acute stroke endovascular intervention by efficiently tracking patient and staff movement during stroke calls.

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. In the time-critical setting of acute stroke, AI offers the tools to rapidly evaluate and consolidate available information, extracting specific predictions from rich, noisy data. It has been applied to the automatic detection of stroke lesions on imaging and can guide treatment decisions through the prediction of tissue outcomes and long-term functional outcomes. This review examines the current state of AI applications in stroke, exploring their potential to reform stroke care through clinical decision support, as well as the challenges and limitations which must be addressed to facilitate their acceptance and adoption for clinical use.

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. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.

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. METHODS: Retrospective review of 40 patients who underwent PD for periampullary tumours between January 2014 and December 2016 was conducted. The patients were matched 1:1 for age, gender, body mass index, Charlson comorbidty index, tumour size and haematological indices. Peri-operative outcomes were evaluated. RESULTS: LAPD appeared to have a longer median operative time as compared to OPD (LAPD, 425 min (285-597) versus OPD, 369 min (260-500)) (P = 0.066). Intra-operative blood loss was comparable between both groups. Respiratory complications were five times higher in the OPD group (LAPD, 5% versus OPD, 25%) (P = 0.077), while LAPD patients required less time to start ambulating post-operatively (LAPD, 2 days versus OPD, 2 days) (P = 0.021). Pancreas-specific complications and morbidity/mortality rates were similar. CONCLUSION: LAPD is a safe alternative to OPD in a select group of patients for an institution starting out with minimally invasive PD, and can be used to bridge the learning curve required for total laparoscopic PD.