After standard surgery for neck hernias, about 25% of patients report low satisfaction. This thesis applied inferential statistics to clinical data and Machine Learning to medical imaging, with the... Show moreAfter standard surgery for neck hernias, about 25% of patients report low satisfaction. This thesis applied inferential statistics to clinical data and Machine Learning to medical imaging, with the goal of finding out where differences in functional outcomes after surgery come from and how artificial intelligence can improve the diagnostic and prognostic process. The initial idea that differences in functional recovery were due to surgical technique was refuted by an RCT from this dissertation. The differences in functional recovery between three different surgical groups (removal of the intervertebral disc without artificial material, placement of intervertebral disc prosthesis, and fusion of vertebrae with a cage) were found to be minimal. It was striking that not surgical technique, but patients' mental health and preoperative, radiological imaging were found to be predictive of clinical recovery after surgery. Although the intervertebral disc prosthesis did not deliver on the promise of preserving mobility and thus could not prevent degeneration at adjacent levels, using Deep Learning based solely on the preoperative MRI of the neck, researchers were able to predict, among other things, which patients would require reoperation after surgery for that adjacent degeneration. The Deep Learning model did that significantly better than an experienced neuroradiologist and neurosurgeon. Such Deep Learning models eliminate the need for time-consuming questionnaires and are thus more cost-effective and less stressful for the patient, while they can be used to identify radiological features important for predicting the postoperative course. After validation with larger radiological datasets, these models can support clinical decision-making and help physicians develop personalized treatment strategies. Challenges within image analysis research for the spine lies in integrating different models into one automated process, preferably built into the electronic health record. Show less
Sbrollini, A.; Barocci, M.; Mancinelli, M.; Paris, M.; Raffaelli, S.; Marcantoni, I.; ... ; Burattini, L. 2023
Heart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP... Show moreHeart failure (HF) diagnosis, typically visually performed by serial electrocardiography, may be supported by machine-learning approaches. Repeated structuring & learning procedure (RS & LP) is a constructive algorithm able to automatically create artificial neural networks (ANN); it relies on three parameters, namely maximal number of hidden layers (MNL), initializations (MNI) and confirmations (MNC), arbitrarily set by the user. The aim of this study is to evaluate RS & LP robustness to varying values of parameters and to identify an optimized combination of parameter values for HF diagnosis. To this aim, the Leiden University Medical Center HF data-base was used. The database is constituted by 129 serial ECG pairs acquired in patients who experienced myocardial infarction; 48 patients developed HF at follow-up (cases), while 81 remained clinically stable (controls). Overall, 15 ANNs were created by considering 13 serial ECG features as inputs (extracted from each serial ECG pair), 2 classes as outputs (cases/controls), and varying values of MNL (1, 2, 3, 4 and 10), MNI (50, 250, 500, 1000 and 1500) and MNC (2, 5, 10, 20 and 50). The area under the curve (AUC) of the receiver operating characteristic did not significantly vary with varying parameter values (P >= 0.09). The optimized combination of parameter values, identified as the one showing the highest AUC, was obtained for MNL = 3, MNI = 500 and MNC = 50 (AUC = 86 %; ANN structure: 3 hidden layers of 14, 14 and 13 neurons, respectively). Thus, RS & LP is robust, and the optimized ANN represents a potentially useful clinical tool for a reliable auto-matic HF diagnosis. Show less
Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to... Show moreBackground: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (classprobability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (rho pred) were calculated. Results: Low to high prediction correlations (rho pred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course. Show less
