Particulate matter exposure from motorized traffic and risk of conversion from mild cognitive impairment to dementia: An Italian prospective cohort study.

BACKGROUND: Based on epidemiologic and laboratory studies, exposure to air pollutants has been linked to many adverse health effects including a higher risk of dementia. In this study, we aimed to evaluate the effect of long-term exposure to outdoor air pollution on risk of conversion to dementia in a cohort of subjects with mild cognitive impairment (MCI). METHODS: We recruited 53 Italian subjects newly-diagnosed with MCI. Within a geographical information system, we assessed recent outdoor air pollutant exposure, by modeling air levels of particulate matter with equivalent aerodynamic diameter ≤10 µm (PM10) from motorized traffic at participants' residence. We investigated the relation of PM10 concentrations to subsequent conversion from MCI to any type of dementia. Using a Cox-proportional hazards model combined with a restricted cubic spline model, we computed the hazard ratio (HR) of dementia with its 95% confidence interval (CI) according to increasing PM10 exposure, adjusting for sex, age, and educational attainment. RESULTS: During a median follow up of 47.3 months, 34 participants developed dementia, in 26 cases diagnosed as Alzheimer's dementia. In non-linear restricted spline regression analysis, mean and maximum annual PM10 levels positively correlated with cerebrospinal fluid total and phosphorylated tau proteins concentrations, while they were inversely associated with ß-amyloid. Concerning the risk of dementia, we found a positive association starting from above 10 µg/m3 for mean PM10 levels and above 35 µg/m3 for maximum PM10 levels. Specific estimates for Alzheimer's dementia were substantially similar. Adding other potential confounders to the multivariable model or removing early cases of dementia onset during the follow-up had little effect on the estimates. CONCLUSIONS: Our findings suggest that exposure to outdoor air pollutants, PM10 in particular, may non-linearly increase conversion from MCI to dementia above a certain ambient air concentration.

Residential exposure to magnetic fields from high-voltage power lines and risk of childhood leukemia.

BACKGROUND: Several studies have suggested an excess risk of leukemia among children living close to high-voltage power lines and exposed to magnetic fields. However, not all studies have yielded consistent results, and many studies may have been susceptible to confounding and exposure misclassification. METHODS: We conducted a case-control study to investigate the risk of leukemia associated with magnetic field exposure from high-voltage power lines. Eligible participants were children aged 0-15 years residing in the Northern Italian provinces of Modena and Reggio Emilia. We included all 182 registry-identified childhood leukemia cases diagnosed in 1998-2019, and 726 age-, sex- and province-matched population controls. We assessed exposure by calculating distance from house to nearest power line and magnetic field intensity modelling at the subjects' residence. We used conditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders (distance from nearest petrol station and fuel supply within the 1000 m-buffer, traffic-related particulate and benzene concentrations, presence of indoor transformers, percentage of urban area and arable crops). RESULTS: In multivariable analyses, the OR comparing children living <100 m from high-voltage power-lines with children living ≥400 m from power-lines was 2.0 (95% CI 0.8-5.0). Results did not differ substantially by age at disease diagnosis, disease subtype, or when exposure was based on modeled magnetic field intensity, though estimates were imprecise. Spline regression analysis showed an excess risk for both overall leukemia and acute lymphoblastic leukemia among children with residential distances <100 m from power lines, with a monotonic inverse association below this cutpoint. CONCLUSIONS: In this Italian population, close proximity to high-voltage power lines was associated with an excess risk of childhood leukemia.

The assistance of BAZAR robot promotes improved upper limb motor coordination in workers performing an actual use-case manual material handling.

This study aims at evaluating upper limb muscle coordination and activation in workers performing an actual use-case manual material handling (MMH). The study relies on the comparison of the workers' muscular activity while they perform the task, with and without the help of a dual-arm cobot (BAZAR). Eleven participants performed the task and the flexors and extensors muscles of the shoulder, elbow, wrist, and trunk joints were recorded using bipolar electromyography. The results showed that, when the particular MMH was carried out with BAZAR, both upper limb and trunk muscular co-activation and activation were decreased. Therefore, technologies that enable human-robot collaboration (HRC), which share a workspace with employees, relieve employees of external loads and enhance the effectiveness and calibre of task completion. Additionally, these technologies improve the worker's coordination, lessen the physical effort required to interact with the robot, and have a favourable impact on his or her physiological motor strategy. Practitioner summary: Upper limb and trunk muscle co-activation and activation is reduced when a specific manual material handling was performed with a cobot than without it. By improving coordination, reducing physical effort, and changing motor strategy, cobots could be proposed as an ergonomic intervention to lower workers' biomechanical risk in industry.

A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies.

AIMS: The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. METHODS: Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. RESULTS: We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. CONCLUSIONS: These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures.

Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This feature makes it suitable for inexpensive human-robot interaction in social or industrial settings. We employ a pose-driven spatial attention strategy, which guides our proposed Static and Dynamic gestures Network-StaDNet. From the image of the human upper body, we estimate his/her depth, along with the region-of-interest around his/her hands. The Convolutional Neural Network (CNN) in StaDNet is fine-tuned on a background-substituted hand gestures dataset. It is utilized to detect 10 static gestures for each hand as well as to obtain the hand image-embeddings. These are subsequently fused with the augmented pose vector and then passed to the stacked Long Short-Term Memory blocks. Thus, human-centred frame-wise information from the augmented pose vector and from the left/right hands image-embeddings are aggregated in time to predict the dynamic gestures of the performing person. In a number of experiments, we show that the proposed approach surpasses the state-of-the-art results on the large-scale Chalearn 2016 dataset. Moreover, we transfer the knowledge learned through the proposed methodology to the Praxis gestures dataset, and the obtained results also outscore the state-of-the-art on this dataset.

The Sensor-Based Biomechanical Risk Assessment at the Base of the Need for Revising of Standards for Human Ergonomics.

Due to the epochal changes introduced by "Industry 4.0", it is getting harder to apply the varying approaches for biomechanical risk assessment of manual handling tasks used to prevent work-related musculoskeletal disorders (WMDs) considered within the International Standards for ergonomics. In fact, the innovative human-robot collaboration (HRC) systems are widening the number of work motor tasks that cannot be assessed. On the other hand, new sensor-based tools for biomechanical risk assessment could be used for both quantitative "direct instrumental evaluations" and "rating of standard methods", allowing certain improvements over traditional methods. In this light, this Letter aims at detecting the need for revising the standards for human ergonomics and biomechanical risk assessment by analyzing the WMDs prevalence and incidence; additionally, the strengths and weaknesses of traditional methods listed within the International Standards for manual handling activities and the next challenges needed for their revision are considered. As a representative example, the discussion is referred to the lifting of heavy loads where the revision should include the use of sensor-based tools for biomechanical risk assessment during lifting performed with the use of exoskeletons, by more than one person (team lifting) and when the traditional methods cannot be applied. The wearability of sensing and feedback sensors in addition to human augmentation technologies allows for increasing workers' awareness about possible risks and enhance the effectiveness and safety during the execution of in many manual handling activities.

Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study.

Progressive functional decline in the epilepsies is largely unexplained. We formed the ENIGMA-Epilepsy consortium to understand factors that influence brain measures in epilepsy, pooling data from 24 research centres in 14 countries across Europe, North and South America, Asia, and Australia. Structural brain measures were extracted from MRI brain scans across 2149 individuals with epilepsy, divided into four epilepsy subgroups including idiopathic generalized epilepsies (n =367), mesial temporal lobe epilepsies with hippocampal sclerosis (MTLE; left, n = 415; right, n = 339), and all other epilepsies in aggregate (n = 1026), and compared to 1727 matched healthy controls. We ranked brain structures in order of greatest differences between patients and controls, by meta-analysing effect sizes across 16 subcortical and 68 cortical brain regions. We also tested effects of duration of disease, age at onset, and age-by-diagnosis interactions on structural measures. We observed widespread patterns of altered subcortical volume and reduced cortical grey matter thickness. Compared to controls, all epilepsy groups showed lower volume in the right thalamus (Cohen's d = -0.24 to -0.73; P < 1.49 × 10-4), and lower thickness in the precentral gyri bilaterally (d = -0.34 to -0.52; P < 4.31 × 10-6). Both MTLE subgroups showed profound volume reduction in the ipsilateral hippocampus (d = -1.73 to -1.91, P < 1.4 × 10-19), and lower thickness in extrahippocampal cortical regions, including the precentral and paracentral gyri, compared to controls (d = -0.36 to -0.52; P < 1.49 × 10-4). Thickness differences of the ipsilateral temporopolar, parahippocampal, entorhinal, and fusiform gyri, contralateral pars triangularis, and bilateral precuneus, superior frontal and caudal middle frontal gyri were observed in left, but not right, MTLE (d = -0.29 to -0.54; P < 1.49 × 10-4). Contrastingly, thickness differences of the ipsilateral pars opercularis, and contralateral transverse temporal gyrus, were observed in right, but not left, MTLE (d = -0.27 to -0.51; P < 1.49 × 10-4). Lower subcortical volume and cortical thickness associated with a longer duration of epilepsy in the all-epilepsies, all-other-epilepsies, and right MTLE groups (beta, b < -0.0018; P < 1.49 × 10-4). In the largest neuroimaging study of epilepsy to date, we provide information on the common epilepsies that could not be realistically acquired in any other way. Our study provides a robust ranking of brain measures that can be further targeted for study in genetic and neuropathological studies. This worldwide initiative identifies patterns of shared grey matter reduction across epilepsy syndromes, and distinctive abnormalities between epilepsy syndromes, which inform our understanding of epilepsy as a network disorder, and indicate that certain epilepsy syndromes involve more widespread structural compromise than previously assumed.

Artificial Intelligence for the Objective Evaluation of Acne Investigator Global Assessment.

INTRODUCTION: The evaluation of Acne using ordinal scales reflects the clinical perception of severity but has shown low reproducibility both intra- and inter-rater. In this study, we investigated if Artificial Intelligence trained on images of Acne patients could perform acne grading with high accuracy and reliabilities superior to those of expert physicians. METHODS: 479 patients with acne grading ranging from clear to severe and sampled from three ethnic groups participated in this study. Multi-polarization images of facial skin of each patient were acquired from five different angles using the visible spectrum. An Artificial Intelligence was trained using the acquired images to output automatically a measure of Acne severity in the 0-4 numerical range of the Investigator Global Assessment (IGA). RESULTS: The Artificial Intelligence recognized the IGA of a patient with an accuracy of 0.854 and a correlation between manual and automatized evaluation of r=0.958 (P less than .001). DISCUSSION: This is the first work where an Artificial Intelligence was able to directly classify acne patients according to an IGA ordinal scale with high accuracy, no human intervention and no need to count lesions. J Drugs Dermatol. 2018;17(9):1006-1009.

Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson's disease.

Motor phenotypes of Parkinson's disease (PD) are recognized to have different prognosis and therapeutic response, but the neural basis for this clinical heterogeneity remains largely unknown. The main aim of this study was to compare differences in structural connectivity metrics of the main motor network between tremor-dominant and nontremor PD phenotypes (TD-PD and NT-PD, respectively) using probabilistic tractography-based network analysis. A total of 63 PD patients (35 TD-PD patients and 28 NT-PD patients) and 30 healthy controls underwent a 3 T MRI. Next, probabilistic tractography-based network analysis was performed to assess structural connectivity in cerebello-thalamo-basal ganglia-cortical circuits, by measuring the connectivity indices of each tract and the efficiency of each node. Furthermore, dopamine transporter single-photon emission computed tomography (DAT-SPECT) with 123 I-ioflupane was used to assess dopaminergic striatal depletion in all PD patients. Both PD phenotypes showed nodal abnormalities in the substantia nigra, in agreement with DAT-SPECT evaluation. In addition, NT-PD patients displayed connectivity alterations in nigro-pallidal and fronto-striatal pathways, compared with both controls and TD-PD patients, in which the same motor connections seemed to be relatively spared. Of note, in NT-PD group, rigidity-bradykinesia score correlated with fronto-striatal connectivity abnormalities. These findings demonstrate that structural connectivity alterations occur in the cortico-basal ganglia circuit of NT-PD patients, but not in TD-PD patients, suggesting that these anatomical differences may underlie different motor phenotypes of PD. Hum Brain Mapp 38:4716-4729, 2017. © 2017 Wiley Periodicals, Inc.

Alterations of putaminal shape in de novo Parkinson's disease.

INTRODUCTION: Several neuroimaging studies have been carried out to gain insight on the pathological processes that cause PD, but literature findings are inconsistent. The aim of this study was to combine information carried by functional imaging with DA transporter ligands and structural MRI. METHODS: Forty-two untreated, de novo-PD patients and 30 control subjects were involved in this study. Patients were divided in subgroups according to the presence of uni- or bilateral reduction of ligand uptake in the putamen, as observed on DA transporter single-photon emission tomography: 12 patients had abnormal uptake in the right putamen and 11 in the left, whereas 19 had bilateral abnormal uptake. Voxel-based morphometry and shape analysis were used to compare healthy subjects to all de novo-PD or to patients with either right or left abnormal uptake. RESULTS: Shape analysis identified significant differences between de novo-PD and controls in putaminal regions. In patients with unilateral abnormal uptake, only the medial surface of the structure was involved. When patients with bilateral uptake reduction were also considered, changes extended from the medial to the lateral surface of putamina. Voxel-based morphometry showed similar results to those detected with shape analysis, but it failed to identify the putaminal subfield involved in patients with asymmetric or symmetric damage on DA transporter single-photon emission tomography. CONCLUSIONS: Shape analysis in de novo-PD patients suggested a progressive medial-to-lateral involvement of the putamina that paralleled an asymmetric-to-bilateral distribution of DA transporter depletion. © 2016 International Parkinson and Movement Disorder Society.

Integrity of the corpus callosum in patients with benign temporal lobe epilepsy.

OBJECTIVE: Corpus callosum (CC) abnormalities are frequently reported in patients with refractory mesial temporal lobe epilepsy (rMTLE). However, whether CC structural alterations are related to the epileptic syndrome itself or to refractoriness is still unknown. Thus, we aimed to compare patterns of CC change in patients with rMTLE and benign MTLE (bMTLE), the latter of which represents a useful resource to better disentangle factors that contribute to refractoriness. METHODS: The study group included 79 patients with bMTLE (mean age 43.2 ± 14. 8 years), 61 with rMTLE (mean age 45.2 ± 12.4 years) and 134 healthy volunteers. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) were performed to measure thickness, mean diffusivity (MD), and fractional anisotropy (FA) over 50 regions of interest along the cross-sectional CC profile. Statistical analysis comprised analysis of variance (ANOVA) followed by post hoc Tukey's Honest Significant Difference test. RESULTS: We found that all imaging metrics of the CC splenium were altered in rMTLE patients compared to bMTLE and controls. We also found significantly reduced thickness and FA of the anterior CC in rMTLE compared to controls and that FA was reduced only in rMTLE compared to bMTLE. Patients with bMTLE did not differ from controls. Differences between disease subgroups were found in the midbody composed of sensorimotor fibers. SIGNIFICANCE: We found altered multimodal imaging metrics of the CC in rMTLE but not in bMTLE. These findings were independent of the radiologic presence of hippocampal sclerosis, suggesting that differences in the distribution of such alterations might be related to refractoriness.

Determinants of serum cadmium levels in a Northern Italy community: A cross-sectional study.

INTRODUCTION: Cadmium (Cd) is a heavy metal and a serious environmental hazard to humans. Some uncertainties still exist about major sources of Cd exposure in non-occupationally exposed subjects in addition to cigarette smoking, such as diet and outdoor air pollution. We sought to determine the influence of these sources on a biomarker of exposure, serum Cd concentration. METHODS: We recruited 51 randomly selected residents from an Italian urban community, from whom we obtained detailed information about dietary habits and smoking habits, and a blood sample for serum Cd determination. We also assessed outdoor air Cd exposure, by modeling outdoor air levels of particulate matter ≤10µm (PM10) from motorized traffic at geocoded subjects' residence. RESULTS: In crude analysis, regression beta coefficients for dietary Cd, smoking and PM10 on serum Cd levels were 0.03 (95% CI -0.83 to 0.88), 6.96 (95% CI -0.02 to 13.95) and 0.62 (95% CI -0.19 to 1.43), respectively. In the adjusted analysis, regression beta coefficients were -0.34 (95% CI -1-40 to 0.71), 5.81 (95% CI -1.43 to 13.04) and 0.47 (95% CI -0.35 to 1.29), respectively. CONCLUSION: Cigarette smoking was the most important factor influencing serum Cd in our non-occupationally exposed population, as expected, while dietary Cd was not associated with this biomarker. Outdoor air pollution, as assessed through exposure to particulate matter generated by motorized traffic, was an additional source of Cd exposure.

The Three Laws of Neurorobotics: A Review on What Neurorehabilitation Robots Should Do for Patients and Clinicians.

Most studies and reviews on robots for neurorehabilitation focus on their effectiveness. These studies often report inconsistent results. This and many other reasons limit the credit given to these robots by therapists and patients. Further, neurorehabilitation is often still based on therapists' expertise, with competition among different schools of thought, generating substantial uncertainty about what exactly a neurorehabilitation robot should do. Little attention has been given to ethics. This review adopts a new approach, inspired by Asimov's three laws of robotics and based on the most recent studies in neurorobotics, for proposing new guidelines for designing and using robots for neurorehabilitation. We propose three laws of neurorobotics based on the ethical need for safe and effective robots, the redefinition of their role as therapist helpers, and the need for clear and transparent human-machine interfaces. These laws may allow engineers and clinicians to work closely together on a new generation of neurorobots.

White matter abnormalities differentiate severe from benign temporal lobe epilepsy.

OBJECTIVE: Temporal and extratemporal white matter abnormalities have been identified frequently in patients with refractory mesial temporal lobe epilepsy (rMTLE). However, the identification of potential water diffusion abnormalities in patients with drug-responsive, benign MTLE (bMTLE) is still missing. The aim of this study was to identify markers of refractoriness in MTLE. METHODS: The study group included 48 patients with bMTLE (mean age 42.8 + 13.5 years), 38 with rMTLE (mean age 41.7 + 14.1 years) and 54 healthy volunteers. Diffusion tensor imaging (DTI) was performed to measure mean diffusivity (MD) and fractional anisotropy (FA) in a regions-of-interest analysis comprising hippocampi and temporal lobe gray and white matter regions. The presence of hippocampal sclerosis (Hs) was assessed using automated magnetic resonance imaging (MRI) evaluation. For statistics we used chi-square test; two-tailed, two-sample t-test; and stratified linear regression. RESULTS: The significant demographic differences between the two patient groups were sex (p = 0.003), duration of epilepsy (p = 0.003) and complex febrile convulsions (p = 0.0001). In rMTLE, temporal white matter MD was higher and FA lower, as compared to bMTLE. The analysis of diagnostic accuracy (area under the receiver operator characteristic [ROC] curve [AUC]) showed that FA had an AUC for discriminating patients affected from those unaffected by refractory MTLE of 74.0% (p < 0.001), a value that was higher than that of temporal MD (64.0%), hippocampus volume (65.0%), and Hs (66.0%). SIGNIFICANCE: We performed DTI measurements in MTLE and found a significant reduction of FA along the white matter of the temporal lobes in rMTLE, suggesting it as a valuable measure of refractoriness in MTLE.

Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease.

BACKGROUND: The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET). METHODS: We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth. RESULTS: SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%. CONCLUSIONS: SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level.

Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy.

BACKGROUND: The aim of the current study was to distinguish patients with Parkinson disease (PD) from those with progressive supranuclear palsy (PSP) at the individual level using pattern recognition of magnetic resonance imaging data. METHODS: We combined diffusion tensor imaging and voxel-based morphometry in a support vector machine algorithm to evaluate 21 patients with PSP and 57 patients with PD. RESULTS: The automated algorithm correctly distinguished patients who had PD from those who had PSP with 100% accuracy. This accuracy value was obtained when white matter atrophy was considered. Diffusion parameters combined with gray matter atrophy exhibited 90% sensitivity and 96% specificity. CONCLUSIONS: Our findings demonstrate that automated pattern recognition can help distinguish patients with PSP from those with PD on an individual basis.

Diffusion tensor MRI changes in gray structures of the frontal-subcortical circuits in amyotrophic lateral sclerosis.

In this study, we used an automated segmentation of regions of interest and co-registration to diffusion tensor imaging (DTI) images to investigate whether microstructural abnormalities occur in gray structures of the frontal-subcortical circuits in patients with amyotrophic lateral sclerosis (ALS). Twenty-four patients with probable or definite sporadic ALS and 22 healthy controls were enrolled in the study. Thirteen out of 24 ALS patients and all of the control subjects underwent a detailed neuropsychological evaluation. DTI was performed to measure mean diffusivity (MD) and fractional anisotropy in the frontal cortex, caudate, putamen, globus pallidus, thalamus, amygdala and hippocampus. MD values of ALS patients were significantly higher in the frontal cortex (P = 0.023), caudate (P = 0.01), thalamus (P = 0.019), amygdala (P = 0.012) and hippocampus (P = 0.002) compared to controls. MD of these structures significantly correlated to a variable degree with neurological disability and neuropsychological dysfunctions. The increased MD values in several cortical and subcortical gray structures and their correlations with neuropsychological variables substantiate a multisystemic degeneration in ALS and suggest that dysfunctions of frontal-subcortical circuits could play a pivotal role in frontal impairment and behavioral symptoms in ALS patients.

Assessing clinical efficacy of polyp detection models using open-access datasets.

Background: Ensuring accurate polyp detection during colonoscopy is essential for preventing colorectal cancer (CRC). Recent advances in deep learning-based computer-aided detection (CADe) systems have shown promise in enhancing endoscopists' performances. Effective CADe systems must achieve high polyp detection rates from the initial seconds of polyp appearance while maintaining low false positive (FP) detection rates throughout the procedure. Method: We integrated four open-access datasets into a unified platform containing over 340,000 images from various centers, including 380 annotated polyps, with distinct data splits for comprehensive model development and benchmarking. The REAL-Colon dataset, comprising 60 full-procedure colonoscopy videos from six centers, is used as the fifth dataset of the platform to simulate clinical conditions for model evaluation on unseen center data. Performance assessment includes traditional object detection metrics and new metrics that better meet clinical needs. Specifically, by defining detection events as sequences of consecutive detections, we compute per-polyp recall at early detection stages and average per-patient FPs, enabling the generation of Free-Response Receiver Operating Characteristic (FROC) curves. Results: Using YOLOv7, we trained and tested several models across the proposed data splits, showcasing the robustness of our open-access platform for CADe system development and benchmarking. The introduction of new metrics allows for the optimization of CADe operational parameters based on clinically relevant criteria, such as per-patient FPs and early polyp detection. Our findings also reveal that omitting full-procedure videos leads to non-realistic assessments and that detecting small polyp bounding boxes poses the greatest challenge. Conclusion: This study demonstrates how newly available open-access data supports ongoing research progress in environments that closely mimic clinical settings. The introduced metrics and FROC curves illustrate CADe clinical efficacy and can aid in tuning CADe hyperparameters.

REAL-Colon: A dataset for developing real-world AI applications in colonoscopy.

Detection and diagnosis of colon polyps are key to preventing colorectal cancer. Recent evidence suggests that AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems can enhance endoscopists' performance and boost colonoscopy effectiveness. However, most available public datasets primarily consist of still images or video clips, often at a down-sampled resolution, and do not accurately represent real-world colonoscopy procedures. We introduce the REAL-Colon (Real-world multi-center Endoscopy Annotated video Library) dataset: a compilation of 2.7 M native video frames from sixty full-resolution, real-world colonoscopy recordings across multiple centers. The dataset contains 350k bounding-box annotations, each created under the supervision of expert gastroenterologists. Comprehensive patient clinical data, colonoscopy acquisition information, and polyp histopathological information are also included in each video. With its unprecedented size, quality, and heterogeneity, the REAL-Colon dataset is a unique resource for researchers and developers aiming to advance AI research in colonoscopy. Its openness and transparency facilitate rigorous and reproducible research, fostering the development and benchmarking of more accurate and reliable colonoscopy-related algorithms and models.

Seeking Huntington disease biomarkers by multimodal, cross-sectional basal ganglia imaging.

Neurodegeneration of the striatum in Huntington disease (HD) is characterized by loss of medium-spiny neurons, huntingtin nuclear inclusions, reactive gliosis, and iron accumulation. Neuroimaging allows in vivo detection of the macro- and micro-structural changes that occur from presymptomatic stages of the disease (preHD). The aim of our study was to evaluate the reliability of multimodal imaging as an in vivo biomarker of vulnerability and development of the disease and to characterize macro- and micro-structural changes in subcortical nuclei in HD. Macrostructure (T1-weighted images), microstructure (diffusion tensor imaging), and iron content (R 2* relaxometry) of subcortical nuclei and medial temporal lobe structures were evaluated by a 3 T scanner in 17 preHD carriers, 12 early-stage patients and 29 matched controls. We observed a volume reduction and microstructural changes in the basal ganglia (caudate, putamen, and globus pallidus) and iron accumulation in the globus pallidus in both preHD and symptomatic subjects; all these features were significantly more pronounced in patients, in whom degeneration extended to the other subcortical nuclei (i.e., thalamus and accumbens). Mean diffusivity (MD) was the most powerful predictor in models explaining more than 50% of the variability in HD development in the caudate, putamen, and thalamus. These findings suggest that the measurement of MD may further enhance the well-known predictive value of striatal volume to assess disease progression as it is highly sensitive to tissue microimpairment. Multimodal imaging may detect brain changes even in preHD stages.