Molecular medicinal insights into scaffold hopping-based drug discovery success

In both academia and the pharmaceutical industry, innovative hypotheses, methodologies and technologies that can shorten the drug research and development, leading to higher success rates, are vital. In this review, we demonstrate how innovative variations of the scaffold-hopping strategy have been used to create new druggable molecular spaces, drugs, clinical candidates, preclinical candidates, and bioactive agents. We also analyze molecular modulations that enabled improvements of the pharmacodynamic (PD), physiochemical, and pharmacokinetic (PK) properties (P3 properties) of the drugs resulting from these scaffold-hopping strategies.

Comparison of computer-aided and manual measurements in the evaluation of carpal alignment

The purpose of this study was to compare computer-aided analysis and different methods of manual measurements in the evaluation of carpal alignment. The radioscaphoid, radiolunate, radiocapitate and radiometacarpal angles were measured on cone-beam computed tomography (CT) scans of 30 healthy wrists by automated software (Disior Ltd.) and by hand surgeons using lateral radiographs reconstructed from the CT data. Hand surgeons were either given (n = 6) or not given (n = 7) prior instructions on how to perform the measurements. Inter- and intra-observer reliability of manual measurements ranged from good to excellent (intra-class correlation coefficients [ICC] 0.77-0.99), being highest in specialists with standardized methods and in reconstructed radiographs with bone overlap digitally removed. Computer-aided software provided excellent intra-observer reliability (ICC 0.94-1.00) consistently and values that were highly comparable (mean difference range 1°-7°) with the manual measurements made in optimal settings. Computer-aided software provides an accurate and repeatable method to measure carpal alignment in CT scans, minimizing observational errors.

Effectiveness of occupational therapy-led computer-aided interventions on function among adults with conditions of the hand, wrist, and forearm: A systematic literature review and meta-analysis

Introduction

Upper extremity injuries are common, and often treated by occupational therapists. The need to evaluate the effectiveness of occupational therapy interventions to guide practice is pertinent. This systematic review and meta-analysis investigate the effectiveness of occupational therapy-led computer-aided interventions among adults with conditions of the hand, wrist, and forearm.

Methods

A systematic literature search of five databases was undertaken for randomized studies examining occupational therapy-led computer-aided interventions for the treatment of hand, wrist, and forearm conditions. The primary outcome was function, with secondary outcomes of pain, grip and pinch strength. The quality of the included studies was independently assessed using the Cochrane Risk of Bias V2 tool. Meta-analyses were completed.

Results

Three randomized controlled trials were included with 176 participants. One study reported on app use on a tablet and two studies reported on computer gaming. Participants had a variety of hand and wrist diagnoses, treated both conservatively and operatively. There is limited evidence demonstrating that computer-based interventions are as effective as other occupational therapy-led interventions in improving function, pain, grip and pinch strength post-intervention, including small effect size following meta-analysis: grip strength (Fixed Effects Model, SMD 0.13, 95% CI 2.63; -2.36, I2 = 0%) and pinch strength (Fixed Effects Model, SMD -0.12, 95% CI 1.25; -1.50, I2 = 11%).

Conclusions

Limited evidence was found to support the use of computer-aided interventions for adults with a hand, wrist or forearm injury. Further high-quality research is recommended inclusive of a broader range of technologies and a broader range of clinical and patient-reported outcome measures.

A survey on utilization and barriers of digital removable prostheses in the US dental education

PURPOSE

Gaining knowledge on the extent of digital technology implementation in dental education and the barriers to it will help inform future directions to promote the use of such technology and will enhance dental education. This study aimed to investigate the utilization of digitally fabricated removable prostheses and the potential obstacles to implementing such technology in US dental schools.

METHODS

A survey was developed and distributed to the restorative dentistry department chairs and postdoctoral prosthodontic program directors. The survey delivery protocol included follow-up emails 1 week, 3 weeks, and 4 weeks after the initial email. The collected data were analyzed descriptively.

RESULTS

The response rate was 85% and 45% for predoctoral and postdoctoral prosthodontic programs, respectively. The results showed that 88.06% of the predoctoral programs and 95.65% of the postdoctoral prosthodontic programs implement digital complete dentures in the curriculum; however, the digital removable partial dentures implementation rate was recorded at 70.77% in predoctoral programs and 61.9% in postdoctoral prosthodontic programs.

CONCLUSIONS

Dental schools are challenged by cost, design software limitations, IT and laboratory support, and faculty training. Multifaceted support is instrumental in further implementing digital removable prosthodontics into dental education.

Association of ulnar variance with three-dimensional carpal alignment and demographics in asymptomatic volunteers

BACKGROUND

Several carpal pathologies are considered to be related to ulnar variance. Recently, computer-aided computed tomography (CT) analysis software was introduced to quantify three-dimensional (3D) carpal alignment with high accuracy and reliability.

PURPOSE

To determine the association of ulnar variance with 3D carpal alignment and demographics.

MATERIAL AND METHODS

A wrist of 121 asymptomatic volunteers (69 men, 52 women; mean age = 38 ± 10.4 years) was imaged in the neutral wrist position with cone-beam CT. Computer-aided CT analysis software (Bonelogic), based on segmentation and numerical modelling, was used to define ulnar variance and standardized 3D axes for all carpal bones. The association of ulnar variance with 3D carpal alignment, age of the volunteer, and side and dominance of the imaged wrist was assessed.

RESULTS

The mean ulnar variance was -1.6 ± 1.5 mm (range = -5.3 to 2.4 mm). The mean ulnar variance was -1.9 mm and -1.1 mm in men and women (P = 0.007), respectively. Of the imaged 121 wrists, 18 (15%) had positive and 103 (85%) negative ulnar variance. There was no association between ulnar variance and any of the radio- or intercarpal angle values in either the sagittal or coronal plane (ρ = -0.16…0.17, r = -0.13….0.12). The ulnar variance showed no association with side (P = 0.51) or dominance (P = 0.27) of the imaged wrist.

CONCLUSION

3D carpal alignment is not affected by ulnar variance. The association of ulnar variance with sex may in part explain the difference in reported prevalence of some carpal pathologies, such as ulnar impaction syndrome and Kienböck's disease.

Three-dimensional carpal alignment: computer-aided CT analysis of carpal axes and normal ranges

Assessment of carpal alignment traditionally uses carpal bone axes measured on lateral radiographs. In this study, three-dimensional axes were defined for carpal bones using segmentation and numerical modelling of CT data of 121 neutrally positioned, asymptomatic wrists. The geometric axis was used for radius, scaphoid and capitate, whereas the axis based on a line perpendicular to the articular surface was used for the other carpal bones. Normal values of radiocarpal angles in the radial coordinate and the reliability of the computer-aided analysis are reported. The mean sagittal radiocarpal angles (positive in palmar direction) were as follows: scaphoid 58° (SD 10°), lunate 0° (SD 11°), triquetrum 12° (SD 8°), trapezium 17° (SD 8°), trapezoid -10° (SD 7°), capitate -17° (SD 9°) and hamate 2° (SD 7°). The mean coronal radiocarpal angles (positive in ulnar direction) were -42° (SD 9°), -20° (SD 4°), -49° (SD 4°), -32° (SD 6°), -16° (SD 5°), 2° (SD 7°) and 8° (SD 6°), respectively. The intra-observer reliability of the measurements was excellent (mean intraclass correlations coefficient 0.98). This study provides guidelines on how to measure and quantify carpal alignment three-dimensionally, and a database for the normal values. Together, these may be useful when analysing various wrist pathologies and kinematics of the wrist.

Deep learning for osteoarthritis classification in temporomandibular joint

OBJECTIVES

This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases.

SUBJECTS AND METHODS

A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ-OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet-B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient-weighted class activation mapping (grad-CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists).

RESULTS

The classification accuracies of ResNet-152 and EfficientNet-B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad-CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed.

CONCLUSIONS

The artificial intelligence model improved the diagnostic power of TMJ-OA when trained with two-dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ-OA.

Gamified Physical Rehabilitation for Older Adults With Musculoskeletal Issues: Pilot Noninferiority Randomized Clinical Trial

BACKGROUND

Resource-rich countries are facing the challenge of aging societies, a high risk of dependence, and a high cost of care. Researchers attempted to address these issues by using cost-efficient, innovative technology to promote healthy aging and regain functionality. After an injury, efficient rehabilitation is crucial to promote returning home and prevent institutionalization. However, there is often a lack of motivation to carry out physical therapies. Consequently, there is a growing interest in testing new approaches like gamified physical rehabilitation to achieve functional targets and prevent rehospitalization.

OBJECTIVE

The purpose of this study is to assess the effectiveness of a personal mobility device compared with standard care in the rehabilitation treatment of patients with musculoskeletal issues.

METHODS

A total of 57 patients aged 67-95 years were randomly assigned to the intervention group (n=35) using the gamified rehabilitation equipment 3 times a week or to the control group (n=22) receiving usual standard care. Due to dropout, only 41 patients were included in the postintervention analysis. Outcome measures included the short physical performance battery (SPPB), isometric hand grip strength (IHGS), functional independence measure (FIM), and the number of steps.

RESULTS

A noninferiority related to the primary outcome (SPPB) was identified during the hospital stay, and no significant differences were found between the control and intervention groups for any of the secondary outcomes (IHGS, FIM, or steps), which demonstrates the potential of the serious game-based intervention to be as effective as the standard physical rehabilitation at the hospital. The analysis by mixed-effects regression on SPPB showed a group×time interaction (SPPB_I_t1=-0.77, 95% CI -2.03 to 0.50, P=.23; SPPB_I_t2=0.21, 95% CI -1.07 to 0.48, P=.75). Although not significant, a positive IHGS improvement of more than 2 kg (Right: 2.52 kg, 95% CI -0.72 to 5.37, P=.13; Left: 2.43 kg, 95% CI -0.18 to 4.23, P=.07) for the patient from the intervention group was observed.

CONCLUSIONS

Serious game-based rehabilitation could potentially be an effective alternative for older patients to regain their functional capacities.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03847454; https://clinicaltrials.gov/ct2/show/NCT03847454.

The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study

BACKGROUND

Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals.

OBJECTIVE

The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients.

METHODS

We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined.

RESULTS

A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected.

CONCLUSIONS

Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.

Teaching, Learning and Assessing Anatomy with Artificial Intelligence: The Road to a Better Future

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.

Influence of the Residual Ridge Widths and Implant Thread Designs on Implant Positioning Using Static Implant Guided Surgery

PURPOSE

Aggressive implant macrothread designs have been widely used. However, the effects of the aggressive thread design on the accuracy of static guided surgery, especially in a case of narrow residual ridge, have not been well-studied. The aim of this study was to evaluate the effects of two different implant macrothread designs and the residual ridge widths on the accuracy of tooth-supported static guided implant surgery.

MATERIALS AND METHODS

Forty implant fixtures with two different macrodesigns: a conventional thread design bone level tapered (BLT), and an aggressive thread design bone level tapered (BLX) were placed in 40 simulated polyurethane models with narrow and wide residual ridges. The placed implant positions were compared with the planned implant position and angulational deviation, as well as three-dimensional (3D) deviations at the entry and apex of the implant were measured. One-way ANOVA with Tukey's multiple comparisons (ɑ = 0.05) were used to determine level of significance between the mean and variance deviation values. 95% confidence intervals and box plots were used to demonstrate the means and ranges of precision.

RESULTS

In terms of angulational deviation, there was no statistically significant difference in the mean deviations for both types of implants, p = 1.55 and p = 0.84 for wide and narrow ridge groups, respectively. However, the range of deviation was much larger in the narrow ridge of the BLX group compared to the BLT group. In both narrow ridge and wide ridge, the BLX group had lower mean 3D deviation values at both the entry and the apex with statistically significant differences for both entry point of the wide ridge (p = 0.027) and narrow ridge (p = 0.022) as well as at the apex of the wide ridge (p = 0.006) but not the apex of the narrow ridge (p = 0.142).

CONCLUSION

The aggressive larger thread design of dental implants may influence the accuracy of implant placement more than the ridge dimension.

Discovering the first US FDA-approved computer-aided polyp detection system

Colorectal cancer is the third most common cancer worldwide. Because of the slow progression of the precancerous precursors, an efficient endoscopic surveillance strategy may be expected. It seems that around one-fourth of colorectal malignancies are still missed during colonoscopy. Several endoscopic technologies have been introduced, without radical changes. Interest in the development of artificial intelligence applications in the medical field has grown in the past decade. Artificial intelligence can help to highlight a specific region of interest that needs closer examination for the identification of polyps. The aim of this review is to report the first clinical experiences with the first US FDA-approved, real-time, deep-learning, computer-aided detection system (GI Genius™, Medtronic).

RETRACTED: Skin cancer diagnosis based on a hybrid AlexNet/extreme learning machine optimized by Fractional-order Red Fox Optimization algorithm

Skin cancer is known as one of the most usual malignant cancer in the human body. Statistics show that each year above one million people are added to the who has this cancer. There are different types of skin cancer, where, the main difference is on the type of cell that is developing cancer. The best way to treat this cancer is to diagnose it early and to prevent the lesion from spreading with surgery. Early detection and treatment of skin cancer from skin images can significantly reduce mortality. Although skin cancer is very dangerous, early diagnosis and appropriate treatment, in most cases, prevent death. The present study introduces a new diagnostic technique for skin cancer based on deep learning and metaheuristics. At first, a pre-trained modified AlexNet based on batch normalization layers is used to train the skin dermoscopy images. Afterward, the last several layers are substituted by an Extreme Learning Machine (ELM). For providing higher efficiency in the ELM network, a newly amended metaheuristic, called Fractional-order Red Fox Optimization (FORFO) Algorithm is used. The final results of the proposed technique are compared with some various techniques and the results showed the effectiveness of the suggested method.

Accuracy of Different Surgical Guide Designs for Static Computer-Assisted Implant Surgery: An in vitro Study

The aims of this study were to evaluate the effect of (i) the different surgical guide designs and (ii) implant placement location on the accuracy of fully guided implant placement in single edentulous sites using an in vitro study model. Forty-five partially edentulous models were scanned and divided into three groups: group 1, tooth-supported full-arch surgical guide; group 2, three different tooth-supported shortened surgical guides (SSGs); and group 3, tooth-supported full-arch surgical guide with a crossbar. All surgical guides were printed and used for fully guided implant placement. A total of 180 implants (60 per group) were placed, and scanbodies were positioned on all models, and postoperative surface scan files (STL) files were obtained. Superimposition of preoperative and postoperative STL files was performed, and the accuracy of implant position was evaluated. The interaction between group and implant location was statistically significant for angle, 3D offset at the base, and at the tip (p<0.001). The post-hoc tests showed a statistically significantly higher deviation for group 2 compared to group 3 for all outcomes for implants #4 (p<0.05) and #7 (p<0.05). There was also a statistically significant difference in all outcomes between groups 1 and 3 for implant #7 (p<0.05). All surgical guide designs presented satisfactory performance with clinically acceptable levels of deviation. However, SSGs presented higher accuracy for guided implant placement in a single-edentulous site, whereas a full-arch surgical guide with a crossbar presented superior outcomes when two or more guided implants were placed simultaneously.

Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study

Along with recent developments in deep learning techniques, computer-aided diagnosis (CAD) has been growing rapidly in the medical imaging field. In this work, we evaluate the deep learning-based CAD algorithm (DCAD) for detecting and localizing 3 major thoracic abnormalities visible on chest radiographs (CR) and to compare the performance of physicians with and without the assistance of the algorithm. A subset of 244 subjects (60% abnormal CRs) was evaluated. Abnormal findings included mass/nodules (55%), consolidation (21%), and pneumothorax (24%). Observer performance tests were conducted to assess whether the performance of physicians could be enhanced with the algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) and the area under the jackknife alternative free-response ROC (JAFROC) were measured to evaluate the performance of the algorithm and physicians in image classification and lesion detection, respectively. The AUCs for nodule/mass, consolidation, and pneumothorax were 0.9883, 1.000, and 0.9997, respectively. For the image classification, the overall AUC of the pooled physicians was 0.8679 without DCAD and 0.9112 with DCAD. Regarding lesion detection, the pooled observers exhibited a weighted JAFROC figure of merit (FOM) of 0.8426 without DCAD and 0.9112 with DCAD. DCAD for CRs could enhance physicians' performance in the detection of 3 major thoracic abnormalities.

Computer-Aided Segmentation and Machine Learning of Integrated Clinical and Diffusion-Weighted Imaging Parameters for Predicting Lymph Node Metastasis in Endometrial Cancer

Simple Summary

Computer-aided segmentation and machine learning added values of clinical parameters and diffusion-weighted imaging radiomics for predicting nodal metastasis in endometrial cancer, with a diagnostic performance superior to criteria based on lymph node size or apparent diffusion coefficient.

Abstract

Precise risk stratification in lymphadenectomy is important for patients with endometrial cancer (EC), to balance the therapeutic benefit against the operation-related morbidity and mortality. We aimed to investigate added values of computer-aided segmentation and machine learning based on clinical parameters and diffusion-weighted imaging radiomics for predicting lymph node (LN) metastasis in EC. This prospective observational study included 236 women with EC (mean age ± standard deviation, 51.2 ± 11.6 years) who underwent magnetic resonance (MR) imaging before surgery during July 2010–July 2018, randomly split into training (n = 165) and test sets (n = 71). A decision-tree model was constructed based on mean apparent diffusion coefficient (ADC) value of the tumor (cutoff, 1.1 × 10⁻³ mm²/s), skewness of the relative ADC value (cutoff, 1.2), short-axis diameter of LN (cutoff, 1.7 mm) and skewness ADC value of the LN (cutoff, 7.2 × 10⁻²), as well as tumor grade (1 vs. 2 and 3), and clinical tumor size (cutoff, 20 mm). The sensitivity and specificity of the model were 94% and 80% for the training set and 86%, 78% for the independent testing set, respectively. The areas under the receiver operating characteristics curve (AUCs) of the decision-tree was 0.85—significantly higher than the mean ADC model (AUC = 0.54) and LN short-axis diameter criteria (AUC = 0.62) (both p < 0.0001). We concluded that a combination of clinical and MR radiomics generates a prediction model for LN metastasis in EC, with diagnostic performance surpassing the conventional ADC and size criteria.

Computer-aided assessment of the chemokine receptors CXCR3, CXCR4 and CXCR7 expression in gallbladder carcinoma

Gallbladder carcinoma (GBC) is a vicious and invasive disease. The major challenge in the clinical treatment of GBC is the lack of a suitable prognosis method. Chemokine receptors such as CXCR3, CXCR4 and CXCR7 play vital roles in the process of tumour progression and metastasis. Their expression levels and distribution are proven to be indicative of the progression of GBC, but are hard to be decoded by conventional pathological methods, and therefore, not commonly used in the prognosis of GBC. In this study, we developed a computer‐aided image analysis method, which we used to quantitatively measure the expression levels of CXCR3, CXCR4 and CXCR7 in the nuclei and cytoplasm of glandular and interstitial cells from a cohort of 55 GBC patients. We found that CXCR3, CXCR4 and CXCR7 expressions are associated with the clinicopathological variables of GBC. Cytoplasmic CXCR3, nuclear CXCR7 and cytoplasmic CXCR7 were significant predictive factors of histology invasion, whereas cytoplasmic CXCR4 and nuclear CXCR4 were significantly correlated with T and N stage and were associated with the overall survival and disease‐free survival. These results suggest that the quantification and localisation of CXCR3, CXCR4 and CXCR7 expressions in different cell types should be considered using computer‐aided assessment to improve the accuracy of prognosis in GBC.

Comparison of the postoperative and follow-up accuracy of articulator model surgery and virtual surgical planning in skeletal class III patients

The aim of this study was to evaluate the postoperative and follow-up accuracy of using an intermediate occlusal splint between articulator model surgery (AMS) and virtual surgical planning (VSP) in double-jaw operations. Thirty skeletal class III patients were randomly allocated to have AMS or VSP. In the AMS group surgical planning was done through conventional articulator model surgery, and an intermediate occlusal splint made of acrylic resin was used. In the VSP group the surgical simulation was done virtually, and the same intermediate splint was used in the software and then fabricated using rapid prototyping technology. Preoperatively, one week postoperatively, and 1∼2-years later we obtained follow-up cone-beam computed tomographic (CT) images of each patient. Absolute linear differences between planned and actual outcomes, as well as planned and follow-up outcomes, were evaluated. There was no significant difference in either postoperative accuracy or follow-up accuracy between the methods, and there was no significant difference in the rate of skeletal relapse. Planning transfer by intermediate splint might therefore be the dominant factor in the final inaccuracies. The potentially greater accuracy of VSP may be realised with the help of new positioning devices instead of an intermediate splint.

Determining the Accuracy of the Mandibular Canal Region in 3D Biomodels Fabricated from CBCT Scanned Data: A Cadaveric Study

OBJECTIVE

To validate the accuracy of the mandibular canal region in 3D biomodel produced by using data obtained from Cone-Beam Computed Tomography (CBCT) of cadaveric mandibles.

METHODS

Six hemi-mandible samples were scanned using the i-CAT CBCT system. The scanned data was transferred to the OsiriX software for measurement protocol and subsequently into Mimics software to fabricate customized cutting jigs and 3D biomodels based on rapid prototyping technology. The hemi-mandibles were segmented into 5 dentoalveolar blocks using the customized jigs. Digital calliper was used to measure six distances surrounding the mandibular canal on each section. The same distances were measured on the corresponding cross-sectional OsiriX images and the 3D biomodels of each dentoalveolar block.

RESULTS

Statistically no significant difference was found when measurements from OsiriX images and 3D biomodels were compared to the "gold standard" -direct digital calliper measurement of the cadaveric dentoalveolar blocks. Moreover, the mean value difference of the various measurements between the different study components was also minimal.

CONCLUSION

Various distances surrounding the mandibular canal from 3D biomodels produced from the CBCT scanned data was similar to that of direct digital calliper measurements of the cadaveric specimens.

Synthesis and Evaluation of Aryl Substituted Propyl Piperazines for Potential Atypical Antipsychotic Activity

BACKGROUND

Schizophrenia is a disorder with complex etiology with hyperdopaminergia as the leading underlying cause. Atypical antipsychotics are the agents which do not give rise to significant extrapyramidal side effects and are more effective against negative symptoms of schizophrenia.

INTRODUCTION

A new series of chloro-substituted substituted aryloxypiperazine derivatives and their indole based derivatives was designed and evaluated for atypical antipsychotic activity based on established models for combined dopaminergic and serotonergic antagonism.

METHODS

The present series of compounds were designed based on 3D similarity studies, synthesized and evaluated for atypical antipsychotic activity in animal models for combined dopaminergic and serotonergic antagonism. The blood-brain barrier penetration potential was assessed from theoretical log BB values computed through an online software program.

RESULTS

Theoretical ADME profiling of the designed compounds based on selected physicochemical parameters suggested excellent compliance with Lipinski's rules. The log BB values obtained for the compounds suggested a good potential for brain permeation. Indole substitution contributed towards an improved efficacy over aryloxy analogs. Lead compounds showed a potential for combined dopaminergic and serotonergic antagonism.

CONCLUSION

The 5-methoxy indole based compounds 16 and 17 were identified as the lead compounds displaying a potential atypical antipsychotic profile.

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early-stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false-negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real-time gastroscopy to provide on-site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.

A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of breast masses on ultrasound: Added value for the inexperienced breast radiologist

To evaluate the value of the computer-aided diagnosis (CAD) program applied to diagnostic breast ultrasonography (US) based on operator experience.US images of 100 breast masses from 91 women over 2 months (from May to June 2015) were collected and retrospectively analyzed. Three less experienced and 2 experienced breast imaging radiologists analyzed the US features of the breast masses without and with CAD according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. We then compared the diagnostic performance between the experienced and less experienced radiologists and analyzed the interobserver agreement among the radiologists.Of the 100 breast masses, 41 (41%) were malignant and 59 (59%) were benign. Compared with the experienced radiologists, the less experienced radiologists had significantly improved negative predictive value (86.7%-94.7% vs 53.3%-76.2%, respectively) and area under receiver operating characteristics curve (0.823-0.839 vs 0.623-0.759, respectively) with CAD assistance (all P < .05). In contrast, experienced radiologists had significantly improved specificity (52.5% and 54.2% vs 66.1% and 66.1%) and positive predictive value (55.6% and 58.5% vs 64.9% and 64.9%, respectively) with CAD assistance (all P < .05). Interobserver variability of US features and final assessment by categories were significantly improved and moderate agreement was seen in the final assessment after CAD combination regardless of the radiologist's experience.CAD is a useful additional diagnostic tool for breast US in all radiologists, with benefits differing depending on the radiologist's level of experience. In this study, CAD improved the interobserver agreement and showed acceptable agreement in the characterization of breast masses.

An innovative method of ocular prosthesis fabrication by bio-CAD and rapid 3-D printing technology: A pilot study

Ocular prosthesis is either a readymade stock shell or custom made prosthesis (CMP). Presently, there is no other technology available, which is either superior or even comparable to the conventional CMP. The present study was designed to fabricate ocular prosthesis using computer aided design (CAD) and rapid manufacturing (RM) technology and to compare it with custom made prosthesis (CMP). The ocular prosthesis prepared by CAD was compared with conventional CMP in terms of time taken for fabrication, weight, cosmesis, comfort, and motility. Two eyes of two patients were included. Computerized tomography scan of wax model of socket was converted into three dimensional format using Materialize Interactive Medical Image Control System (MIMICS)software and further refined. This was given as an input to rapid manufacturing machine (Polyjet 3-D printer). The final painting on prototype was done by an ocularist. The average effective time required for fabrication of CAD prosthesis was 2.5 hours; and weight 2.9 grams. The same for CMP were 10 hours; and 4.4 grams. CAD prosthesis was more comfortable for both the patients. The study demonstrates the first ever attempt of fabricating a complete ocular prosthesis using CAD and rapid manufacturing and comparing it with conventional CMP. This prosthesis takes lesser time for fabrication, and is more comfortable. Studies with larger sample size will be required to further validate this technique.

Ultrasound Strain Elastography for Breast Lesions: Computer-Aided Evaluation With Quantifiable Elastographic Features

OBJECTIVES

To develop and evaluate a set of quantifiable elastographic features based on ultrasound real-time strain elastography (SE) in differentiating between benign and malignant breast lesions.

METHODS

The SE and conventional B-mode ultrasound images of 226 breast lesions (81 malignant, 145 benign) were obtained from 226 consecutive women. By using a computer-aided tool, four elastographic features (elasticity score, lesion stiffness degree, lesion-to-fat strain ratio, and elastography-to-B-mode lesion area ratio) were respectively calculated and evaluated. Histopathologic results were used as the reference standard. B-mode Breast Imaging Reporting and Data System categorization was used to compare the performances between B-mode ultrasound and SE. Sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic curve analyses were performed to evaluate the diagnostic performances for three data sets (conventional B-mode ultrasound alone, SE features alone, combined SE features).

RESULTS

Quantifiable SE features for malignant lesions all showed significantly higher values than those for benign lesions (all P < .001). The evaluation with any individual SE feature significantly improved the specificity in breast lesion differentiation compared with B-mode ultrasound (all P <.001). The logistic regression model combing SE features significantly improved the diagnostic performance compared with B-mode US, with significantly increased specificity (95.2% versus 54.5%; P < .001) and area under the receiver operating characteristic curve (0.988 versus 0.921, P < .001).

CONCLUSIONS

Computer-aided tool with SE provided further elasticity information for breast characterization. Evaluation using quantifiable SE features showed better diagnostic performance than conventional B-mode ultrasound in breast lesion differentiation.

Increasing stability of antibody via antibody engineering: stability engineering on an anti-hVEGF

Antibody stability is very important for expression, activity, specificity, and storage. This knowledge of antibody structure has made it possible for a computer-aided molecule design to be used to optimize and increase antibody stability. Many computational methods have been built based on knowledge or structure, however, a good integrated engineering system has yet to be developed that combines these methods. In the current study, we designed an integrated computer-aided engineering protocol, which included several successful methods. Mutants were designed considering factors that affected stability and multiwall filter screening was used to improve the design accuracy. Using this protocol, the thermo-stability of an anti-hVEGF antibody was significantly improved. Nearly 40% of the single-point mutants proved to be more stable than the parent antibody and most of the mutations could be stacked effectively. The T₅₀ also improved about 7°C by combinational mutation of seven sites in the light chain and three sites in the heavy chain. Data indicate that the protocol is an effective method for optimization of antibody structure, especially for improving thermo-stability. This protocol could also be used to enhance the stability of other antibodies.