The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology

Radiographic parameters of the digit in a cohort population of amiata donkeys

Background: The most common musculoskeletal conditions reported in donkeys are related to the foot. Radiographic examinations are clinically important in the diagnosis of foot abnormalities and are commonly used. However, few studies have been conducted to establish the normal radiographic appearance of a donkey’s foot. Aim: To determine the radiographic features of the front digit in healthy Amiata donkeys. Methods: Radiographic examinations were performed on 56 forefeet of 28 Amiata donkeys. Three radiographic views of each front foot were taken: lateromedial, dorsopalmar and dorso-65°proximal/palmarodistal oblique. Seventeen angular and linear radiographic parameters and the crena solearis were evaluated in all forefeet, and 18 morphometric parameters were evaluated in 16 out of 56 forefeet. Statistical analysis was carried out on all the measures assessed. Results: The radiographic appearance of the forefoot was ascertained, and data were reported as median ± standard error, minimum and maximum values. No statistical differences were obtained between the right and left forefeet. Conclusion: The normal baseline parameters of the forefeet of Amiata donkeys were recorded and described and compared with other donkey breeds and horses. The findings highlighted that the donkey breed affects the radiographic parameters of the digit

Presencia de Bryopsida fértil en los niveles Westfalianos del subgrup Itararé, Cuenca de Paraná, Brasil

The bryophyte fossils are rare, mainly in Paleozoic sedimentary rocks in spite of being present since the Silurian Period. In the Division Bryophyta, the fossils that belong to the Class Bryopsida are recognized since the Carboniferous, but they are extremely scarce. They are plentiful only in Permian sediments, in the Petchora, Kuznetsk and Russian Platform basins, also in Antarctica, Karoo basin (the last in South Africa) and India. Identified at the genus Dwykea, gametophyte specimens bearing pleurocarpous sporophyte were recovered from the lowermost levels of Itararé Subgroup, near Campinas city, S. Paulo State. These fossils correspond to the first register of bryophyte female gametophyte for the Carboniferous Period. The microflora in association with these fossils allow correlations of these levels to the Palynozone Ahrensisporites cristatus of Westphalian age. Related to proglacial sediments, they may correspond to a tundra vegetation covering the Northeastern border of Paraná Basin, during the Westphalian.Los fósiles atribuidos a briófitos son escasos, principalmente en rocas paleozoicas a pesar de ser registrados desde el Silúrico. Para la División Bryophyta, los fósiles correspondientes a la Clase Bryopsida comienzan a ser encontrados a partir del Carbonífero, aunque son bastante escasos. Registros más abundantes son conocidos para el Pérmico en rocas de las cuencas de Petchora, Kuznetsk y en la Plataforma Rusa, así como en la Antártica, en la Cuenca del Karoo (África del Sur) e India. Especímenes de gametófitos con esporofitos pleurocárpicos del género Dwykea fueron colectados en los niveles inferiores del Subgrupo Itararé, próximos a la ciudad de Campinas, Estado de S. Paulo. Estos fósiles corresponden al primer registro para el periodo Carbonífero de gametofitos femeninos fértiles. La microflora asociada a los fósiles de Dwykea permite establecer correlaciones con la Palinozona Ahrensisporites cristatus de edad westfaliana. Además los niveles donde fueron colectados los ejemplares de Dwykea corresponden a sedimentos proglaciales, que son interpretados como una vegetación de tundra que habitaba en el margen noreste de la Cuenca del Paraná, durante el Westfaliano

Predictors of Bone Metastases at68Ga-PSMA-11 PET/CT in Hormone-Sensitive Prostate Cancer (HSPC) Patients with Early Biochemical Recurrence or Persistence

Prostate-specific-membrane-antigen/positron-emission-tomography (PSMA-PET) can accurately detect disease localizations in prostate cancer (PCa) patients with early biochemical recurrence/persistence (BCR/BCP), allowing for more personalized image-guided treatments in oligometastatic patients with major impact in the case of bone metastases (BM). Therefore, this study aimed to identify predictors of BM at PSMA-PET in early-BCR/BCP hormone-sensitive PCa (HSPC) patients, previously treated with radical intent (radiotherapy or radical prostatectomy ± salvage-radiotherapy (SRT)). A retrospective analysis was performed on 443 (68)Ga-PSMA-11-PET/CT scans. The cohort median PSA at PET-scan was 0.60 (IQR: 0.38–1.04) ng/mL. PSMA-PET detection rate was 42.0% (186/443), and distant lesions (M1a/b/c) were found in 17.6% (78/443) of cases. BM (M1b) were present in 9.9% (44/443) of cases, with 70.5% (31/44) showing oligometastatic spread (≤3 PSMA-positive lesions). In the multivariate binary logistic regression model (accuracy: 71.2%, Nagelkerke-R(2): 13%), T stage ≥ 3a (OR: 2.52; 95% CI: 1.13–5.60; p = 0.024), clinical setting (previous SRT vs. first-time BCR OR: 2.90; 95% CI: 1.32–6.35; p = 0.008), and PSAdt (OR: 0.93; 95% CI: 0.88–0.99; p = 0.026) were proven to be significant predictors of bone metastases, with a 7% risk increment for each single-unit decrement of PSAdt. These predictors could be used to further refine the indication for PSMA-PET in early BCR/BCP HSPC patients, leading to higher detection rates of bone disease and more personalized treatments

Comparison of different classifiers to recognize active bone marrow from CT images

One of the main problems during in the treatment of anal cancer with chemotherapy and radiation is the occurrence of Hematologic Toxicity (HT). In particular, during radiotherapy it is crucial to spare Bone Marrow (BM), since the radiation dose received by BM in pelvic bones predicts the onset of HT. In this direction, the most popular strategies are based on the identification of the hematopoietically active BM (actBM), that is the part of BM in charge of blood cells generation, using MRI, SPECT or PET, but no approached have been proposed based on CT. In this study we compare four different classifiers in recognizing actBM from CT images using 36 radiomic features. We used Genetic Algorithms (GAs) to simultaneously optimize the feature subsets and the classifier parameters, separately for three pelvic subregions: iliac bone marrow (IBM), lower pelvis bone marrow (LPBM), and lumbosacral bone marrow (LSBM). The obtained classifiers were applied to CT sequences of a cohort of 25 patients affected by carcinoma of the anal canal. Classifiers results were compared with the actBM identified from 18FDG-PET (reference standard, RS). It emerged that the performances of the 4 classifiers are similar and they are satisfactory for IBM and LSBM subregions (Dice > 0.7) whereas they are poor for LPBM (Dice < 0.5)

Radiomics for identification of active bone marrow from ct: An exploratory study

The radiation dose received by the pelvic Bone Marrow (BM) is a predictive factor for Hematologic Toxicity (HT) occurrence in the treatment of anal cancer. For this reason it is important to avoid BM during radiotherapy. In particular, the standard strategy in these cases consists in the identification of hematopoietically active BM (actBM), i.e. the part of BM in charge of blood cells generation, on 18 FDG-PET, FLT-PET or MRI, but no approached have been developed for identifying actBM from CT images. This exploratory study aims to use radiomics for detecting actBM on CT sequences. Our approach is based on the extraction of 36 first-order and texture (second-order) features for each CT slice. These features are used as input of a Decision Tree (DT) classifier able to discriminate between active and inactive BM regions on the images. This method was applied to five patients affected by carcinoma of the anal canal and the obtained actBM segmentation was compared with the standard actBM identification from 18 FDG-PET (reference standard, RS). Our results show that actBM identification in lumbosacral and iliac structures using radiomics overlaps the RS for more than 75% in 4 out of 5 patients