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1.
J Biomed Inform ; 110: 103553, 2020 10.
Artículo en Inglés | MEDLINE | ID: mdl-32891762

RESUMEN

The development, evaluation, and eventual deployment of novel medical devices is a complex process involving various areas of expertise. Although the need for a User Centred Design approach to the development of both hardware and software has long been established, both current regulatory guidelines and widespread evaluation approaches fail to reflect the challenges encountered during day-to-day clinical practice. As such, the results from these evaluations may not provide a realistic account of the problems encountered by users when introduced to clinical practice. In this paper, we present a case study on designing the evaluation of a novel device to support laparoscopic liver surgery. Through a reflective account of the design of our usability evaluation, we identify and describe seven primary dimensions of ecological validity encountered in clinical usability evaluations. These dimensions are: 'user roles', 'environment', 'training', 'scenario', 'patient involvement', 'software', and 'hardware'. We analyse three recently published clinical usability evaluation articles to assess (and illustrate) the applicability and completeness of these dimensions. Finally, we discuss the compromises encountered during clinical usability evaluations and how to best report on these considerations. The framework presented here aims to further the agenda of ecologically valid evaluation practice, reflecting the constraints of medical practice.


Asunto(s)
Programas Informáticos , Interfaz Usuario-Computador , Humanos
2.
Med Phys ; 50(5): 2695-2704, 2023 May.
Artículo en Inglés | MEDLINE | ID: mdl-36779419

RESUMEN

BACKGROUND: Accurate camera and hand-eye calibration are essential to ensure high-quality results in image-guided surgery applications. The process must also be able to be undertaken by a nonexpert user in a surgical setting. PURPOSE: This work seeks to identify a suitable method for tracked stereo laparoscope calibration within theater. METHODS: A custom calibration rig, to enable rapid calibration in a surgical setting, was designed. The rig was compared against freehand calibration. Stereo reprojection, stereo reconstruction, tracked stereo reprojection, and tracked stereo reconstruction error metrics were used to evaluate calibration quality. RESULTS: Use of the calibration rig reduced mean errors: reprojection (1.47 mm [SD 0.13] vs. 3.14 mm [SD 2.11], p-value 1e-8), reconstruction (1.37 px [SD 0.10] vs. 10.10 px [SD 4.54], p-value 6e-7), and tracked reconstruction (1.38 mm [SD 0.10] vs. 12.64 mm [SD 4.34], p-value 1e-6) compared with freehand calibration. The use of a ChArUco pattern yielded slightly lower reprojection errors, while a dot grid produced lower reconstruction errors and was more robust under strong global illumination. CONCLUSION: The use of the calibration rig results in a statistically significant decrease in calibration error metrics, versus freehand calibration, and represents the preferred approach for use in the operating theater.


Asunto(s)
Calibración , Procesamiento de Imagen Asistido por Computador , Laparoscopios , Laparoscopios/normas , Laparoscopía/instrumentación , Exactitud de los Datos , Dispositivos Ópticos/normas
3.
J Gastrointest Cancer ; 53(2): 460-465, 2022 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-33877570

RESUMEN

PURPOSE: Pancreatic solid pseudopapillary neoplasms (SPNs) are rare borderline tumours mainly affecting young female patients. The number of patients diagnosed with SPNs has increased significantly in the last decades owing to the increased use of cross-sectional imaging investigating different abdominal symptoms, whilst a significant proportion are incidentally discovered during the process of evaluating other pathologies. We herein present our institutional experience of patients with SPN who underwent curative resection focusing on clinical, pathological features, and the long-term outcomes. METHODS: All patients undergoing pancreatectomy in our institution for SPN from January 2010 until December 2018 were included. Clinical, perioperative, histological, and long-term outcomes were collected and analysed. RESULTS: During the inclusion period, a total of 19 patients had a pathological diagnosis of SPNs after surgical resection. Sixteen of them were female (84%), while the median patient age was 30 (range 16-66) years. Nine patients (47%) underwent distal pancreatectomy and splenectomy, 2 (11%) underwent spleen preserving distal pancreatectomy, 6 (32%) underwent pancreatoduodenectomy, one (5%) underwent total pancreatectomy, and one (5%) central pancreatectomy. Seventeen patients underwent R0 resection. During a median follow-up of 23 months, no tumour recurrence or death was recorded. CONCLUSION: In our experience, SPNs are rare tumours with low malignant potentials. Surgical resection remains the gold standard treatment and is associated with good prognosis.


Asunto(s)
Neoplasias Pancreáticas , Adolescente , Adulto , Anciano , Femenino , Humanos , Masculino , Persona de Mediana Edad , Recurrencia Local de Neoplasia/epidemiología , Recurrencia Local de Neoplasia/cirugía , Páncreas/patología , Pancreatectomía/métodos , Neoplasias Pancreáticas/diagnóstico , Neoplasias Pancreáticas/patología , Neoplasias Pancreáticas/cirugía , Estudios Retrospectivos , Adulto Joven
4.
Int J Comput Assist Radiol Surg ; 17(1): 167-176, 2022 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-34697757

RESUMEN

PURPOSE: The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D-2D global registration in laparoscopic liver interventions. METHODS: Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. RESULTS: We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. CONCLUSIONS: Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration.


Asunto(s)
Realidad Aumentada , Laparoscopía , Cirugía Asistida por Computador , Algoritmos , Humanos , Imagenología Tridimensional , Hígado/diagnóstico por imagen , Hígado/cirugía
5.
Int J Comput Assist Radiol Surg ; 16(7): 1151-1160, 2021 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-34046826

RESUMEN

PURPOSE: Registration of Laparoscopic Ultrasound (LUS) to a pre-operative scan such as Computed Tomography (CT) using blood vessel information has been proposed as a method to enable image-guidance for laparoscopic liver resection. Currently, there are solutions for this problem that can potentially enable clinical translation by bypassing the need for a manual initialisation and tracking information. However, no reliable framework for the segmentation of vessels in 2D untracked LUS images has been presented. METHODS: We propose the use of 2D UNet for the segmentation of liver vessels in 2D LUS images. We integrate these results in a previously developed registration method, and show the feasibility of a fully automatic initialisation to the LUS to CT registration problem without a tracking device. RESULTS: We validate our segmentation using LUS data from 6 patients. We test multiple models by placing patient datasets into different combinations of training, testing and hold-out, and obtain mean Dice scores ranging from 0.543 to 0.706. Using these segmentations, we obtain registration accuracies between 6.3 and 16.6 mm in 50% of cases. CONCLUSIONS: We demonstrate the first instance of deep learning (DL) for the segmentation of liver vessels in LUS. Our results show the feasibility of UNet in detecting multiple vessel instances in 2D LUS images, and potentially automating a LUS to CT registration pipeline.


Asunto(s)
Hepatectomía/métodos , Neoplasias Hepáticas/cirugía , Hígado/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , Ultrasonografía/métodos , Humanos , Laparoscopía/métodos , Hígado/cirugía , Neoplasias Hepáticas/diagnóstico
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