Proof-of-concept of a robotic-driven photogrammetric scanner for intra-operative knee cartilage repair

This work presents a proof-of-concept of a robotic-driven intra-operative scanner designed for knee cartilage lesion repair, part of a system for direct in vivo bioprinting. The proposed system is based on a photogrammetric pipeline, which reconstructs the cartilage and lesion surfaces from sets of photographs acquired by a robotic-handled endoscope, and produces 3D grafts for further printing path planning. A validation on a synthetic phantom is presented, showing that, despite the cartilage smooth and featureless surface, the current prototype can accurately reconstruct osteochondral lesions and their surroundings with mean error values of 0.199 ± 0.096 mm but with noticeable concentration on areas with poor lighting or low photographic coverage. The system can also accurately generate grafts for bioprinting, although with a slight tendency to underestimate the actual lesion sizes, producing grafts with coverage errors of -12.2 ± 3.7, -7.9 ± 4.9, and -15.2 ± 3.4% for the medio-lateral, antero-posterior, and craneo-caudal directions, respectively. Improvements in lighting and acquisition for enhancing reconstruction accuracy are planned as future work, as well as integration into a complete bioprinting pipeline and validation with ex vivo phantoms.

DigiHEALTH: Suite of Digital Solutions for Long-Term Healthy and Active Aging

The population in the world is aging dramatically, and therefore, the economic and social effort required to maintain the quality of life is being increased. Assistive technologies are progressively expanding and present great opportunities; however, given the sensitivity of health issues and the vulnerability of older adults, some considerations need to be considered. This paper presents DigiHEALTH, a suite of digital solutions for long-term healthy and active aging. It is the result of a fruitful trajectory of research in healthy aging where we have understood stakeholders' needs, defined the main suite properties (that would allow scalability and interoperability with health services), and codesigned a set of digital solutions by applying a continuous reflexive cycle. At the current stage of development, the digital suite presents eight digital solutions to carry out the following: (a) minimize digital barriers for older adults (authentication system based on face recognition and digital voice assistant), (b) facilitate active and healthy living (well-being assessment module, recommendation system, and personalized nutritional system), and (c) mitigate specific impairments (heart failure decompensation, mobility assessment and correction, and orofacial gesture trainer). The suite is available online and it includes specific details in terms of technology readiness level and specific conditions for usage and acquisition. This live website will be continually updated and enriched with more digital solutions and further experiences of collaboration.

Prediction and Analysis of Heart Failure Decompensation Events Based on Telemonitored Data and Artificial Intelligence Methods

Cardiovascular diseases are the leading cause of death globally, taking an estimated 17.9 million lives each year. Heart failure (HF) occurs when the heart is not able to pump enough blood to satisfy metabolic needs. People diagnosed with chronic HF may suffer from cardiac decompensation events (CDEs), which cause patients' worsening. Being able to intervene before decompensation occurs is the major challenge addressed in this study. The aim of this study is to exploit available patient data to develop an artificial intelligence (AI) model capable of predicting the risk of CDEs timely and accurately. Materials and Methods: The vital variables of patients (n = 488) diagnosed with chronic heart failure were monitored between 2014 and 2022. Several supervised classification models were trained with these monitoring data to predict CDEs, using clinicians' annotations as the gold standard. Feature extraction methods were applied to identify significant variables. Results: The XGBoost classifier achieved an AUC of 0.72 in the cross-validation process and 0.69 in the testing set. The most predictive physiological variables for CAE decompensations are weight gain, oxygen saturation in the final days, and heart rate. Additionally, the answers to questionnaires on wellbeing, orthopnoea, and ankles are strongly significant predictors.

Preoperative Omega-6/Omega-3 Fatty Acid Ratio Could Predict Postoperative Outcomes in Patients with Surgically Resected Non-Small-Cell Lung Cancer

Introduction: The aim of this study was to determine whether preoperative nutritional status and inflammatory status, specifically polyunsaturated acids and the omega 6/3 ratio, would affect postoperative outcomes and complications in patients with lung cancer undergoing lung resection. Methods: This prospective observational study included 68 patients with early-stage non-small-cell lung cancer who were candidates for radical surgery. A complete nutritional assessment was performed. The primary study variable was postoperative complications and mortality in the first 30 days. Descriptive, bivariate, and logistic regression analyses were carried out. Results: A total of 50 men (73.53%) and 18 women (26.47%) underwent surgery, with a median age of 64.2 (±9.74) years. The mean omega 6/3 ratio was 17.39 (±9.45). A complication occurred in 39.7% of the study sample (n = 27), the most common being persistent air leak in 23.53% (n = 16). After performing the bivariate analysis, the only variable that remained significant was the omega 6/3 ratio; we observed that it had a prognostic value for persistent air leak (p = 0.001) independent of age, sex, comorbidity, preoperative respiratory function, and approach or type of surgery. The remaining nutritional and inflammatory markers did not have a statistically significant association (p > 0.05) with postoperative complications. However, this significance was not maintained in the multivariate analysis by a small margin (p = 0.052; 95% CI: 0.77-1.41). Conclusions: Omega 6/3 ratio may be a prognostic factor for air leak, independent of the patient's clinical and pathological characteristics.

Efficacy of hyperthermia pleurodesis: A comparative experimental study on serous membrane of abdominopelvic and thoracic cavities of rats

BACKGROUND

Pleurodesis is a common technique for treating the accumulation of air or liquid in the pleural space caused by pneumothorax or pleural effusion, it is based on the bounding of pleural layers through induced inflammatory lesions. There are several pleurodesis procedures.

OBJECTIVES

To test and describe the inflammatory effect of hyperthermia on the pleural and peritoneal mesothelia of rats, with the aim of testing the effectiveness of this process for inducing pleurodesis.

METHODS

35 Sprague-Dawley (male/female) rats were randomized into four treatment groups: Group A (Talc, 10 individuals); group B (control, 5 individuals); group C (hyperthermic isotonic saline, 10 individuals); and group D (filtrate air at 50°, 10 individuals). Inflammatory effect of hyperthermia was the primary outcome parameter.

RESULTS

In the talc group, minimal adhesions between both pleural and peritoneal layers were observed in seven rats. Talc produced peritoneal mesothelium inflammation and fibrosis associated to foreign body giant cells in 80% (8/10) of the sample. Furthermore, clear evidence of a granulomatous foreign-body reaction was detected. No macroscopic and/or microscopic damage was registered in the remaining three groups (control, hyperthermic, and filtrate air).

CONCLUSIONS

Talc is an excellent method for producing pleuro-peritoneal inflammatory lesions. On the contrary, hyperthermia apparently does not induce the macroscopic and microscopic damage that is required for efficient pleurodesis. Therefore, hyperthermia should not be used for pleurodesis procedures.

Automated anatomical labeling of a topologically variant abdominal arterial system via probabilistic hypergraph matching

Automated anatomical vessel labeling of the abdominal arterial system is a crucial topic in medical image processing. One reason for this is the importance of the abdominal arterial system in the human body, and another is that such labeling is necessary for the related disease diagnoses, treatments and epidemiological population analyses. We define a hypergraph representation of the abdominal arterial system as a family tree model with a probabilistic hypergraph matching framework for automated vessel labeling. Then we treat the labelling problem as the convex optimization problem and solve it with the maximum a posteriori(MAP) combined the likelihood obtained by geometric labelling with the family tree topology-based knowledge. Geometrically, we utilize XGBoost ensemble learning with an intrinsic geometric feature importance analysis for branch-level labeling. In topology, the defined family tree model of the abdominal arterial system is transferred as a Markov chain model using a constrained traversal order method and further the Markov chain model is optimized by a hidden Markov model (HMM). The probability distribution of the target branches for each candidate anatomical name is predicted and effectively embedded in the HMM model. This approach is evaluated with the leave-one-out method on 37 clinical patients' abdominal arteries, and the average accuracy is 91.94%. The obtained results are better than those of the state-of-art method with an F1 score of 93.00% and a recall of 93.00%, as the proposed method simultaneously handles the anatomical structural variability and discriminates between the symmetric branches. It is demonstrated to be suitable for labelling branches of the abdominal arterial system and can also be extended to similar tubular organ networks, such as arterial or airway networks.

Outcomes of surgical resection after neoadjuvant chemoimmunotherapy in locally advanced stage IIIA non-small-cell lung cancer

OBJECTIVES

This analysis aimed to evaluate perioperative outcomes of surgical resection following neoadjuvant treatment with chemotherapy plus nivolumab in resectable stage IIIA non-small-cell lung cancer.

METHODS

Eligible patients received neoadjuvant chemotherapy (paclitaxel + carboplatin) plus nivolumab for 3 cycles. Reassessment of the tumour was carried out after treatment and patients with at least stable disease as best response underwent pulmonary resection. After surgery, patients received adjuvant treatment with nivolumab for 1 year. Surgical data were collected from the NADIM database and patient charts were reviewed for additional surgical details.

RESULTS

Among 46 patients who received neoadjuvant treatment, 41 (89.1%) underwent surgery. Two patients rejected surgery and 3 did not fulfil resectability criteria. There were 35 lobectomies (85.3%), 3 of which were sleeve lobectomies (9.4%), 3 bilobectomies (7.3%) and 3 pneumonectomies (7.3%). Video-assisted thoracoscopy was the initial approach in 51.2% of cases, with a conversion rate of 19% (n = 4). There was no operative mortality at either 30 or 90 days. The most common complications were prolonged air leak (n = 8), pneumonia (n = 5) and arrhythmia (n = 4). Complete resection (R0) was achieved in all patients who underwent surgery, downstaging was observed in 37 patients (90.2%) and major pathological response in 34 patients (82.9%).

CONCLUSIONS

Surgical resection following induction therapy with chemotherapy plus nivolumab appears to be safe and offers appropriate oncological outcomes. Perioperative morbidity and mortality rates in our study were no higher than previously reported in this setting. A minimally invasive approach is, therefore, feasible.

Efficacy of hyperthermia pleurodesis: A comparative experimental study on serous membrane of abdominopelvic and thoracic cavities of rats

BACKGROUND

Pleurodesis is a common technique for treating the accumulation of air or liquid in the pleural space caused by pneumothorax or pleural effusion, it is based on the bounding of pleural layers through induced inflammatory lesions. There are several pleurodesis procedures.

OBJECTIVES

To test and describe the inflammatory effect of hyperthermia on the pleural and peritoneal mesothelia of rats, with the aim of testing the effectiveness of this process for inducing pleurodesis.

METHODS

35 Sprague-Dawley (male/female) rats were randomized into four treatment groups: Group A (Talc, 10 individuals); group B (control, 5 individuals); group C (hyperthermic isotonic saline, 10 individuals); and group D (filtrate air at 50°, 10 individuals). Inflammatory effect of hyperthermia was the primary outcome parameter.

RESULTS

In the talc group, minimal adhesions between both pleural and peritoneal layers were observed in seven rats. Talc produced peritoneal mesothelium inflammation and fibrosis associated to foreign body giant cells in 80% (8/10) of the sample. Furthermore, clear evidence of a granulomatous foreign-body reaction was detected. No macroscopic and/or microscopic damage was registered in the remaining three groups (control, hyperthermic, and filtrate air).

CONCLUSIONS

Talc is an excellent method for producing pleuro-peritoneal inflammatory lesions. On the contrary, hyperthermia apparently does not induce the macroscopic and microscopic damage that is required for efficient pleurodesis. Therefore, hyperthermia should not be used for pleurodesis procedures.

Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study

BACKGROUND

The status of the data-driven management of cancer care as well as the challenges, opportunities, and recommendations aimed at accelerating the rate of progress in this field are topics of great interest. Two international workshops, one conducted in June 2019 in Cordoba, Spain, and one in October 2019 in Athens, Greece, were organized by four Horizon 2020 (H2020) European Union (EU)-funded projects: BOUNCE, CATCH ITN, DESIREE, and MyPal. The issues covered included patient engagement, knowledge and data-driven decision support systems, patient journey, rehabilitation, personalized diagnosis, trust, assessment of guidelines, and interoperability of information and communication technology (ICT) platforms. A series of recommendations was provided as the complex landscape of data-driven technical innovation in cancer care was portrayed.

OBJECTIVE

This study aims to provide information on the current state of the art of technology and data-driven innovations for the management of cancer care through the work of four EU H2020-funded projects.

METHODS

Two international workshops on ICT in the management of cancer care were held, and several topics were identified through discussion among the participants. A focus group was formulated after the second workshop, in which the status of technological and data-driven cancer management as well as the challenges, opportunities, and recommendations in this area were collected and analyzed.

RESULTS

Technical and data-driven innovations provide promising tools for the management of cancer care. However, several challenges must be successfully addressed, such as patient engagement, interoperability of ICT-based systems, knowledge management, and trust. This paper analyzes these challenges, which can be opportunities for further research and practical implementation and can provide practical recommendations for future work.

CONCLUSIONS

Technology and data-driven innovations are becoming an integral part of cancer care management. In this process, specific challenges need to be addressed, such as increasing trust and engaging the whole stakeholder ecosystem, to fully benefit from these innovations.

Image-Based 3D Characterization of Abdominal Aortic Aneurysm Deformation After Endovascular Aneurysm Repair

An abdominal aortic aneurysm (AAA) is a focal dilation of the abdominal aorta, that if not treated, tends to grow and may rupture. The most common treatment for AAAs is the endovascular aneurysm repair (EVAR), which requires that patients undergo Computed Tomography Angiography (CTA)-based post-operative lifelong surveillance due to the possible appearance of complications. These complications may again lead to AAA dilation and rupture. However, there is a lack of advanced quantitative image-analysis tools to support the clinicians in the follow-up. Currently, the approach is to evaluate AAA diameter changes along time to infer the progress of the patient and the post-operative risk of AAA rupture. An increased AAA diameter is usually associated with a higher rupture risk, but there are some small AAAs that rupture, whereas other larger aneurysms remain stable. This means that the diameter-based rupture risk assessment is not suitable for all the cases, and there is increasing evidence that the biomechanical behavior of the AAA may provide additional valuable information regarding the progression of the disease and the risk of rupture. Hence, we propose a promising methodology for post-operative CTA time-series registration and subsequent aneurysm biomechanical strain analysis. From these strains, quantitative image-based descriptors are extracted using a principal component analysis of the tensile and compressive strain fields. Evaluated on 22 patients, our approach yields a mean area under the curve of 88.6% when correlating the strain-based quantitative descriptors with the long-term patient prognosis. This suggests that the strain information directly extracted from the CTA images is able to capture the biomechanical behavior of the aneurysm without relying on finite element modeling and simulation. Furthermore, the extracted descriptors set the basis for possible future imaging biomarkers that may be used in clinical practice. Apart from the diameter, these biomarkers may be used to assess patient prognosis and to enable informed decision making after an EVAR intervention, especially in difficult uncertain cases.

3D Pulmonary Artery Segmentation from CTA Scans Using Deep Learning with Realistic Data Augmentation

The characterization of the vasculature in the mediastinum, more specifically the pulmonary artery, is of vital importance for the evaluation of several pulmonary vascular diseases. Thus, the goal of this study is to automatically segment the pulmonary artery (PA) from computed tomography angiography images, which opens up the opportunity for more complex analysis of the evolution of the PA geometry in health and disease and can be used in complex fluid mechanics models or individualized medicine. For that purpose, a new 3D convolutional neural network architecture is proposed, which is trained on images coming from different patient cohorts. The network makes use a strong data augmentation paradigm based on realistic deformations generated by applying principal component analysis to the deformation fields obtained from the affine registration of several datasets. The network is validated on 91 datasets by comparing the automatic segmentations with semi-automatically delineated ground truths in terms of mean Dice and Jaccard coefficients and mean distance between surfaces, which yields values of 0.89, 0.80 and 1.25 mm, respectively. Finally, a comparison against a Unet architecture is also included.