Neil Singh

CT
Revamping your radiation dose policies for the enterprise
Recent regulatory changes regarding radiation dose management have made it imperative for healthcare providers to revise their imaging policies and procedures. But where should you begin? Neomi Mullens and Neil Singh from Ascendian Healthcare Consulting offer their advice.
June 8, 2014
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CT
Radiation dose safety: Filtering the message from the noise
Patient radiation dose safety is a hot topic in the imaging industry and media. But as often happens when a subject is hot, the radiation safety information pool has become muddied with disparate and conflicting information. Neil Singh from Ascendian Healthcare Consulting helps filter the true message in radiation safety from the industry noise.
April 17, 2014
2013 05 31 14 24 53 75 Ascendian Logo 200
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DEXA reveals tea consumption supports bone health
Moderate-frequency tea consumption was associated with better bone mineral status and more favorable longitudinal changes.
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AI
AI tools may not meet breast radiologists' expectations in practice
Lung Cancer
CT
Stepped reminders boost CT lung cancer screening
Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
AI
Model confidence, reader expertise affect reader-LLM collaboration
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DEXA reveals tea consumption supports bone health
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Moderate-frequency tea consumption was associated with better bone mineral status and more favorable longitudinal changes.
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Stepped reminders boost CT lung cancer screening
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Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
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Ga-68 HER2-PET/CT images before neoadjuvant therapy, at end of second cycle, and before surgery. (A) Baseline scan demonstrated soft-tissue lesion measuring approximately 2.3 × 2.2 cm in upper outer quadrant of left breast, with marked radiotracer uptake and SUVmax of 10.1. (B) At end of second cycle, imaging revealed residual lesion of approximately 2.2 × 1.0 cm in same region, without abnormal radiotracer accumulation. (C) Preoperative imaging demonstrated further reduction in lesion size to approximately 2.0 × 0.6 cm, with no abnormal tracer uptake detected.
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The models could provide a framework for identifying which adolescents are at higher risk of developing psychiatric conditions.
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